{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c862ce1e",
   "metadata": {},
   "source": [
    "Copyright (c) MONAI Consortium  \n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");  \n",
    "you may not use this file except in compliance with the License.  \n",
    "You may obtain a copy of the License at  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;http://www.apache.org/licenses/LICENSE-2.0  \n",
    "Unless required by applicable law or agreed to in writing, software  \n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,  \n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  \n",
    "See the License for the specific language governing permissions and  \n",
    "limitations under the License.\n",
    "\n",
    "# 2D Latent Diffusion Model\n",
    "\n",
    "In this tutorial, we will walk through the process of using the MONAI generative components package to generate synthetic data using Latent Diffusion Models (LDM)  [1, 2]. Specifically, we will focus on training an LDM to create synthetic X-ray images of hands from the MEDNIST dataset.\n",
    "\n",
    "[1] - Rombach et al. \"High-Resolution Image Synthesis with Latent Diffusion Models\" https://arxiv.org/abs/2112.10752\n",
    "\n",
    "[2] - Pinaya et al. \"Brain imaging generation with latent diffusion models\" https://arxiv.org/abs/2209.07162\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6801e8b",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "10499760",
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3da50af",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "424a5eb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 1.3.3rc1+14.g6c23fd06\n",
      "Numpy version: 1.26.4\n",
      "Pytorch version: 2.4.0+cu121\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
      "MONAI rev id: 6c23fd06fc11667beedd0ba730d4104076a8db2d\n",
      "MONAI __file__: /home/<username>/miniconda3/envs/monai1/lib/python3.12/site-packages/monai/__init__.py\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.11\n",
      "ITK version: 5.4.0\n",
      "Nibabel version: 5.2.1\n",
      "scikit-image version: 0.24.0\n",
      "scipy version: 1.14.0\n",
      "Pillow version: 10.4.0\n",
      "Tensorboard version: 2.17.0\n",
      "gdown version: 5.2.0\n",
      "TorchVision version: 0.19.0+cu121\n",
      "tqdm version: 4.66.5\n",
      "lmdb version: 1.5.1\n",
      "psutil version: 5.9.0\n",
      "pandas version: 2.2.2\n",
      "einops version: 0.8.0\n",
      "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "mlflow version: 2.15.1\n",
      "pynrrd version: 1.0.0\n",
      "clearml version: 1.16.3\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "import tempfile\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from monai import transforms\n",
    "from monai.apps import MedNISTDataset\n",
    "from monai.config import print_config\n",
    "from monai.data import DataLoader, Dataset\n",
    "from monai.utils import first, set_determinism, ensure_tuple\n",
    "from torch.amp import GradScaler, autocast\n",
    "from tqdm import tqdm\n",
    "\n",
    "from monai.inferers import LatentDiffusionInferer\n",
    "from monai.losses.adversarial_loss import PatchAdversarialLoss\n",
    "from monai.losses.perceptual import PerceptualLoss\n",
    "from monai.networks.nets import AutoencoderKL, DiffusionModelUNet, PatchDiscriminator\n",
    "from monai.networks.schedulers import DDPMScheduler\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35141304",
   "metadata": {},
   "source": [
    "### Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a52c24b",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4031e219",
   "metadata": {},
   "source": [
    "### Setup a data directory and download dataset\n",
    "Specify a MONAI_DATA_DIRECTORY variable, where the data will be downloaded. If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8a9b6c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/tmp/tmpy_blw935\n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5ad19e",
   "metadata": {},
   "source": [
    "## Prepare training set data loader\n",
    "\n",
    "Here we will download the MEDNIST dataset and prepare the training set data loader. The MEDNIST dataset contains images of different body parts, including the hand, chest, and abdomen, in grayscale format. For this tutorial, we will use only the `Hand` class. We include data augmentation performed by the `RandAffine` transformation in the data transformations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b253ff61",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MedNIST.tar.gz: 59.0MB [00:01, 46.2MB/s]                              "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-08-06 18:30:10,594 - INFO - Downloaded: /tmp/tmpy_blw935/MedNIST.tar.gz\n",
      "2024-08-06 18:30:10,672 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
      "2024-08-06 18:30:10,673 - INFO - Writing into directory: /tmp/tmpy_blw935.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading dataset: 100%|██████████| 47164/47164 [00:12<00:00, 3671.41it/s]\n"
     ]
    }
   ],
   "source": [
    "train_data = MedNISTDataset(root_dir=root_dir, section=\"training\", download=True, seed=0)\n",
    "train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"Hand\"]\n",
    "image_size = 64\n",
    "train_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.LoadImaged(keys=[\"image\"]),\n",
    "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
    "        transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True),\n",
    "        transforms.RandAffined(\n",
    "            keys=[\"image\"],\n",
    "            rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n",
    "            translate_range=[(-1, 1), (-1, 1)],\n",
    "            scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n",
    "            spatial_size=[image_size, image_size],\n",
    "            padding_mode=\"zeros\",\n",
    "            prob=0.5,\n",
    "        ),\n",
    "    ]\n",
    ")\n",
    "train_ds = Dataset(data=train_datalist, transform=train_transforms)\n",
    "train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4, persistent_workers=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e81f2290",
   "metadata": {},
   "source": [
    "### Visualise examples from the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b4e725bb",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ozmYz++677+zm5sam02mGitUfzvdTBmbZ+dr7OHV9tqKB6524v1fMEKYMfwoJxyTP0Oa9I18l8rkkFYFsA9weqj2pz33xplarFZaa8ZmZhZoYmqmvUoYZ4Ps8YJ/3nYJEHTO6muAvf/mLjUYje/funR0cHNhsNrPZbGavXr2y/f39DCuyWCzs6uoqFBTr9/t2dnZmHz9+DNMHXFuXK+rzlpGHMKKPKWUi5DxRG7Kt04o5+E3s2319R+w6ZQAEtkdXu2jSqm+/1kdRu8V3+tx5VQ+fW7Zt04MsLSzDDMQGagrN6cvRZCR/fzbPYc0xiWv+PDKVoS2ZZ6zX6zYcDu33v/+9rddrGw6HYdc02qF1DdbrddiNjzZ4pbrPgPPPV3SM/8x/H9tsyQOWMoqj4MRH+LEoQAcVRvslFB26r2F9DMlrk4JcjNnt7a3t7e3ZfD4P9PvNzY1NJhO7ubmx+Xy+VeIcEmOI9LgYs6dgABBD2WoKTl1fX9v//J//06rVamg7SwN/+9vf2i9/+Uvr9Xq2Xq+t2WzafD63H3/80Tqdji0WC+v3+/aXv/zFfvjhh9AHjHndMIulr2UDlDJ99Nx5A7HgZBsQu8n9NmlL3vmbBIrbBk9IDPSig9StoHYFfgAgqZG/MggAbu9P7jsV+hJBxMbMwH0eIuZEy0YteeI3L/JJf/oZL/j4+Nh++ctfhjnJ6+trGw6H0fXP2kalmGKD4T7KUTQI8yQ1KFMDLXZ8XhtSNH/qOvrbLH9O+jmkDDDY1Khtcu8i1odof29vz1arVabKIwDA7BMrwKoXpg20IuY2kb4/DvGgUK/jrxU7jzGJ8YWFoxqon+qoVCohn6BSqdj+/r51u12bTCa2XC4LN+G6rzwnACjThoduX5l75QER7yM2DQaL2lD2mBhY1fFAgKKOnt/aTgW+ukrmISTPbqa+98dtOpVQJBszA2WNiB7vFUgRlll2zpnfON48Wprz2Qud70hMIlIgV4DKZoeHh/av//W/tn//7/99aNPl5aWdnZ2FXIFWqxWymlVBmIrACPO5GsptJRZl8/82opUYU/fy9405Ke3bVHYzAEqpNUTnjV+S5AG5x3IEKUOq01C3t7e2WCxsOBzaarWyXq9nq9XK2u122AEQVqzT6QQgAKtFtOPLZBdFXmXAntebvPGpzr1arQa2oNVqWbX6aRvs6XQaChIBfMzuEih/8Ytf2L/5N//GhsOh/elPf7Jer2d/+MMf7OLiwprNZqB8dQw+BPDcNlp9aPF2MxWNx47f9B5lvitjm8p8nwouyrSzzLNpEGhmgdFlEzqWlNfrdet0Opmxp/vS8HsymYQCdv5dPAZAKOobP/ZijN6m7Xq0HWQecvB4ajKPyuTePsOacsRmnzJLe72evXnzJjgqIg2dC0JBuIY6RC2pqt8/BFOwibJvcy0FLjE0H7v2pgM3L1J8ainr5GMRxX3vV3SPlAA2dStfqtEBtpiL1+WHgF8i8DLPUWaar0hiYIBraJSleQS6edhkMjEzC3U9eD5NoNzf3w+1FzRHoEhiOq36H5sKeWmyLXC9z7iL3SPFDKT6OHbN+9Drm0os+CSo63Q6tre3Z0dHR0Ef8Qfz+TyAclg4AIWuJHjMdpfVw4fS162KDpVxgPoSOE+P0/lsf07sfn7TET1X52+0Y4iOptOpffz40Wq1WqAYQXxcW6+/XC5DlMX8Es9BjgIGmLLEZnfLVzaVpxgYRe2KARs911N/CiJijiAF3F6CxHRsU8eXoiJTx6ckxrSYWViSV6lUQhQMO9DpdMJyQ6YG1utP+26wnn8ymdhoNPqsYFYR6Es9hzI/2kauw1y9snx6X7O78cjYp2jYZDKx//bf/pvVajX78OGDjcdj+9WvfhXmd3U3THYU1e3G/TPpygLt/xjzxvPEnB3/P4XxT0lMx1JAIMVmbMoWFF0zBUy2idzLShHA1rbFombeI3b76OjIfvOb39j+/r798pe/DMWdzMxms5lNp9NMUInu/eEPf7Bvv/3Wrq6ubDqdPmr9lLKAwOsysqld27gcsafE8xBhzHHo9co21oMB/8LzrsHc62g0CkuzeLHkA3gnzstfr+9yBHjpnMN5GAo1lpvIQzAJZumMcd/nRW2JsS5FiN/fI3Wd55QU0OS7TXQxdn7RvVNsi6czEejKSqUSVrkQFbdarTBdwBJXEvJY78/qGHUgMbCqz533XlPPhYHVqN/3a4xl0+TI5XJp//AP/2Dr9V2CLtN1CpYUDOgqC7/ctcxY8M8Rs1n6bp67aFaRlAUCZXQ1ds0UGxD7O+8+sXFWdhyVPcZfV4VcgXa7bcfHx3Z4eGh/9Vd/FcZWpVKxyWQSmCquARvV7/ft6uoqs1PuY8omDMF927IVGNhWYo3NUxocLmhO703EoNQ+UQeGaTab2bt372w8Hof12H/6059sMBjYH/7wBzs6OgrXG41G9sMPP9hgMAiMwM3NjS2XSzO7q3gGUlQwgWHOex7/bNsoftmINnXtonvGQJ4HcnkAj89j57802RaE5RnAWH9xXOrzlJNRRzSbzezm5sbG47Hd3t6Gypq6zLDVaoUVGxg2nCvn0Y7Y8tjY/fNAoD6PLzak4zbFHKX6gGM+fvxof/jDH+z777+3f/zHf7TZbGb/63/9r1BjYH9/P+QCaW6CAh9WAKV2OOT4vO+eGwSUichj5+Tp6UO0xUsRALuPnSoTkMQ+8+8Wu01w+OHDBxuNRnZzcxN2xKzX62GVC6tYrq+v7erqymazmf3xj3+09+/f23g8zjBWT2nnfPT/ULIxGNC/t+mAIgVQw6FbolKa1VeP0sGqA79er9t0Og2Vyv7+7//ezO6KRfzv//2/w7wQ87P9fj/MEenOa2Z30xK8eH5TQ97MMnkJzxUJ30cpY4686F37Y4pYo5coDwHMzIqnTMpcJ8ZQzWYzWy6X1mg0guGCHQAAtFotM7NwDECBnQKvr68zCYa8m1SSLt9pG1NG2o9VX9wlr058rA/NzM7Ozuzv/u7vQpIhTAeAf39/39brdXg+ZR3W63Uw6rG6C9q/6OlLBa1e8hxyTLfKPFseaM8DI2WvkXdd/Ttma9SOeDuTWt6X1zfKFL9798729vbs7OwsbGffaDTs9evX9vr1a+v3+/btt9/aeDy2f/iHf7DBYGCDwSD4FAUDL022CcS2yhm4z03zjveUpSJzNh7SKmeINzqqXLormtndvOl4PLYff/wxs8SJKIP5SL1WXoTgDVEeFabXLCtlBmqZ42NS5LRTES2/Y6AhxTA8JzAoo6dlIh9/bOpe95FYX2sOwXw+D/OVZNT7fQxIhKKAD6s5SEjUHQH1J+VQODb2t4LjmOTppz6fXmc6nVq1WrXlchlAO1MI2lZd860Ow68f34Rq9QDhJYCFsoDSA4BtgG7sM5hZza/ydk8DJb/jZBkpC3D87zx2S/+mLTc3N2G1Tq1Ws+l0arVaza6vr63Valmv1zMzC9MJZmbffPONHR0d2YcPH6zRaNhsNgtTBc8pDxl83osZ0MZsco1Yw2NAwCfmsd7aL0skClIlheoHIGAMSXT6+PGjfffdd+FeJF6RVYqh8YPLG05tZ2wAeif50LJJpODbVLZdMeMSG6QvnSG4DyDwVOUmxnYTJo0oWwEs+j6ZTGw+n4cEuV6vZ81mM2Tc6/TB/v6+dTodm0wmodBPpVKx2WwWDLYyAzrG/LrrWGU2nocfgArHe5Ch/ebHeezn4uLCzs7OQjvM7sauBx7VajUkEmoEuEnElgdkYCOeQ/JAQOqzMt+lrp9ytp1OJ+yT0W63Q/7Ger0Oy7hhVUnAVpvpEzrznmvTcZY3Vr0e4huGw6ENh8NwfrVatdevX1u32w1JhY1Gw169emXr9dp+85vf2Hq9tr//+7+37777zt69exeW/sae6SnE29z7ykZFh2JGQL8vKzHH4BWFH80HiCH12H01WtDMYj1P8wFQBjY58s7f38eDGkXI/jljTvI+si3Sj32/SVti79r3SdGzPheKTg3UTdsTeyYPYsuCnyLjERtfGB4of6L+SqUSlh+a3TnHdrtt6/U6rKcmoRBDHjPKm4Icf3wsSo/pgR+Pfgx5Z+5pYb0P58WciAcj+n3sXaVs2nPprpdN3s9D3a9S+TTtyrw6egUYIKFTE1i1Up/2tW/7Qz9HTNdiYBQd4u9qtRpWmCkLDGimvkyn07FutxsYuKeQmO95CF/iZaNpgpSDLOoUVQRF2P4hPRBQo0UCoCYIqmHw9+M3iqmrA/gcpUZi0YRnIoig+FFQodnaqcj4Pi/xIYBA3nll2pV655s4wpcoZQFUqqBNLKIqkpgT8mBUhah9NBrZcrm02Wxm19fXIVJrt9t2enoawMDp6aktl0vrdDo2n89tf3/fxuOxnZ+f22AwsMViEeY/eW9aP0PHah4Vq+ObaQg9J9VfqedU0akPbZvmJmjf6Pgs6mv9PxWtqtN4DvEBUFn9uk97/X0qlUrYIr7X64V9JBR8mn1akrdYLMJW8NfX1yHPA/ZAAzWzNCiOfe7PSX3vn99PJcG8qT9TP8EzLZdL+/Dhg5l92mSr2Wxap9Oxb775xgaDQThHV5w9tp7kBal8XmZcxWSrokNFSpl6ufp3mcb6wkFqoHStsqcNdV7LJ/yhEKxh1jb76+jx2vYY9USbUsj0JTvJMm0riphS4OdLkE0NrB+Q95HYWEi1g3Gnc+eNRiNUKTSzEN0QxZEzQ71/VtmQeKfXTTlob2TznHzR6ggfIebZCm+8/RjW77AVsYTIlMRAum9PivV7TnmstuTpoTIDVJBkGpZ8Apiq1Wpl4/E4zMNjp7lm2XETC6A8G1T2mbzzj/3NcYCH6+vrsMSQ8UUlTZ75Oe1crC/voxulwYBPxPERuW9YXqPyogJ1+OpotR1E5P58H9X4ZECzu+h9sViEHdF8u/z/MQXU43RpoX/+mJMsciYvwfCkHEKeo0q9/y8RIJSRmGMs++78uXnn+cJEGCozC0mElFVl7pzMaEqv1mo1Ozw8tEajEcBEvV4PoCA2D++379ZcghRA3qQPVGLGnr8BLbFNY5Qp4N5+jr+s3qUA75dQZ6CM5I3BFAPBZ7PZLDh/VmqQvEouR7vdDjtrNptNWy6XNhgM7Pr62kajkS0Wi/CjOhyzhzE7G4t6vd75aylzpcGlL1pnZiGB/IcffrBGo2HD4dD+/Oc/22q1su+++y4DBK6urjJt8m19bHkMe7rV3gSx3/yd1zn+ZcdeqjpzKJgYWvd5Aev13RyWbu26WCzM7A4EaEJhmXr5nON3Q1Ol434eePh+ew6nWJaFKZL7XOOnAATynj8WwZQ5r2y/eCerTplkLYzv4eFhpuYAUwa1Ws263a7V6/UwvbBarQI48FsA+2W8akxjkdomzF/qmbmvB0laYKhSqQTKWQ27JkDy+SYbGXmWwH/3EgD6fUTt1ibgiB+qW+7t7Vmz2Qxr8lXYe2Jvb89arVZGLyuVSmCmzCyz06RKiubOCzTLgFCvv57FXa8/JcEul0s7Pz+3Wq1mo9HIfvzxx7BHgZmFQkWj0Sjjs55LP4oCy01k410L9f+YY9wEJaWQeAwo6HcpekaNFwYkZhBikYwChbzrxtqX+jvl/D1oSknqug8lmziv1IDzzxg7/0sDApsi/dh7ilHVsettOk5in5PJXal8qp5G9LK/v5+hc4naDg8PwzXYgGU4HGYSqHT+1EtsrMTYAq9feSAg73t/D9qn9Qx8dMhzK6NRRmK6/BKM/X3B+Cb/+8/QLzMLq1fI4QKgUQlSf/OuCNIAB5PJxBaLRSiXrftR5EnZwCbmH/S9orvoB4IeLZdLu7q6ssVi8dlUMmyH1v2gb55THiLg2goMKBrKYwZSUtZAFlFank1Qx4/h0OQjPceXOjWL7y3gwUdscKbmFfPYgE0RelnJi1JT1y5zTgqt6zP6iPFLlJTxTen1phFwGUPsJc9QrtfrUJDn5ubG+v2+mVlgAvb29qzb7VqtVgu7s1WrVet2u9Zut+329jbULKDAD8ZZc2H0mVKsAffNAwB5/8f6Sz/zSx5j96M91F6gyAyf6+9YX8Y+eynTBJvagU1AjNrzGAiaTqdheWqlUgm1/FlBwJr8vb29kFdgZtbpdOz29tY6nY5dX1+Hwj3T6dTM7jb/0WJYmzyntzv6/Hyvx6r+6pJ0AIzZp+kCKgyy8Rebgk2nUxsMBlapfKr6ySZ3z6Ef/rnvC1gfZNfCssxAytmUNZBeWWOMAX/7HAd+q+PWgkQph1hEQfnnzWMGYt/lXTN1Xd+2mJTp59hxZdkKDwR8/5V1pk8hZcBpEZDdtO0PgdQ3uRcZ2/P53CaTiXW7XZtOp9ZqtUJ5YpZHYagXi0VgD0ajUcizUePM9c2yW4376bu8fsszWDHbwf+xa9EOz06oMdZpw7wEwCKmgvOekxmgDQ95nD+nCOSqPdUd/VjRwd+3t582nlqv15+Vpya/YLlchhVi8/k8TBdoMaxqtRplpVK2NPXsMaCgxaj43ic3kmumqw7QJb5D/55zpQltNnviaQIk76beoJaJmmIsQ97gA6WD1jBcek1eNjWmdb0rSHRvby9TrY3roMjaPh8l6TQDCqLfpfpDnz3P8G0TPd5XYs4rz0h4FiAGCF6aFOljESDQYz3IjDmNbaKFIvAZO269Xof52LOzs0D5m5nt7+9bq9WyVqtlBwcHYXnUarUKOx8SpVGJbTweRxNsua/W/acNgIjY2C1jB7zNiCUwIuRAqGHHBgB4qMWgDqmMpEDIc+9aeB8bUOZcb3/9eSSZjkYjm8/ntre3Fyr4tdvtTAJru922/f19azQadnBwEEr9spz78PDQptOpdTodWy6XdnZ2FqYNZrNZsMPaJrW/fmlgzJbq+WqPbm5uApiJrVbjb6ah8DGAAV1+7sHEc0oMKG2qM1tVILyvpKIB/33s87JIPXVcGSoq1j7/O4+VQPKiZ39Mqg2pz8pGsXkRll5j03bEAE0MEJR1sI8tsfuXdbyx8zRK1mt4A7Ot5PVZnsMC7M7n85ABzg6ASuXXajVrNpvW7XbN7BOdu16vw/Ixnev1LJs+mx9LDxmp5IFKZSZ8GzQo4Hnz+rKMLXkpEgNaZVnA1DGx9xc7tlKpZByhmQV2gEgaXQEsEihp2WyEUtNMX1HBkCBLdW/Td5A6nto1CiBjfcrzaqCoietl/dBTyn2DsK2nCcoa+pjD8MdrlF10TT7XvdF19QDno4yUxVQjhVLrbmYeaWrkRzSkxyka5P6pgRoDBLH+8Z8V9es2RqBosPtjyhyv//tzU8/90iWlg+jW3t5eMG7abyThqWOORQ6b0J15OuOvAw3b7/etUqnYaDSy9Xpt7Xbblsul7e/v2/7+vvV6Pet0OvbVV1+FBKj5fG6NRsNGo1HY5AtaeLVafbYpkGdH/O+8vuV3CmDoZxynY98bY18qWde/30deCg2sf+fpjLdjRbYkBai86DtAB5TuZ6qJlQTj8dharZatVquwyoAEVsoad7tdWywW1u12bT6f2/n5eWAHJpNJWA3Gvfy7iNnu1PvmmOVyGfTHj8sy79kXMHpo2SZwKgroysrWzMA2EV/KyWzbAbRBq5CZ3RkGrTOgRsJT/97YxMSv9fZGyLdJ2xkDTnlK+5BSNkKIPf8m0UWKKdC/vxRAkAcEAIYUrWq1WpnngsbU5XqpyFX/vu97p83oNUlQVCYkP4C6BOv1pyW4vV7PGo2GHR0d2Ww2s9lsZmZ3y/JIjiJrmnuok44xJf7vvP5U8aA0pjOx5Wj+OAXwPwW5j36UBQT8XQQM6H9fHZaS2FqmuNvthpoCbKZFXgGrEgjaAKUEa4BqneOnjSmbnRfUcN0UO5wXmHn7/xjy3PZx49UEHkn6ztkmMo0hNJ8QpCif6IwlU8wJeget0ZmvREgEl3oO7sM98xQo1jex51Pjyf9FClmEdFOiLEzM2aeMduw+qeP84NRjFKiR4/GcEVZRJF6mbbABvV7PDg4OAtWuRqbZbAYQynp4ohoAgi97/RD94q9BKdjb21vr9/s2n8+t1WrZbDYLKwdarVbYCrjT6YT8ml6vF4AB273e3NzYeDwOz8bzYcC5P/3gI0kz+wxErNfrz5YoemCtuQOxpK8Y6PTfpQy6n2pQYFN2fLwkybPDeY6uzBiI2QEP9tBz+p1VBtS9WCwW1uv1AhBl6uDVq1e2Wq1sb2/PRqORjcdju7q6CsWK2MZbdc/b9Jiobvj3XASMyrJcD2nTntM+mj0CGEDyHJ6/rneosWuyxIPfrVYrJEBxH61LTkYqCBNDzLzUYrEIlJHPnI45vRTVq9/5ARN7Fo1WiiKn2HH+2DwA49u2CZ2bBzbyDEvMiGJ0n8uophiZImPoz4Pi7PV69urVqwAG1us7+hFjpXOTy+XS1uu1TSaTUCRo24S0MkbezMJyQwoLzefzsFkRSVSHh4cBzLAErNFo2HK5DNndJHZpnfn5fG6LxcImk0kw1hhmLXMMSEKXMOJ+aWKKKfFLG2M2J+bQY4m8eu28vCEP2FO1R16yeFvqP08dmzpGj1Xb4W2cRvMsuatUKqE2Af/rMsRqtWoHBwdmZtZut202m9lgMAj7abTbbbu+vs5MWWHDtfhUzD5vAvy9n1JdivVlUV89pDwVINmqzkBRVFp0vv/MD3BeMM5fs1KVamKJlJllymLqkheUkwxSNYYYcNgDv3Wxn0ooUobUwFMDo8ZOE1hSwKDIcceU2H+f1+9Fx8bOi50ba4tZdrfJ55Ki+8emcWJClIquoY9cg6SkWORyc3MT1mTP5/Ogj5oNHwPGZdqf99y6zn65XFqtVrPZbBYMsa4NZ+yQrU/xIhLEKLLCmnHyDxRsK+PG+NGtbvVzP9ZoszriWHTnp538/yk9jf0dGz++Xc+tv2VkG125j57l6Sn9plNLgEYtB8+Kr06nE2w2eQar1SrsgXB9fR3ABPtqaC4L19JcgE2fqSjwidnkp5K8QCql09vI1hUItxUcog42bwi5F3OanU7H3r59GxJPmK+l5KqZhXlcnc9V4wzNNB6Pg1KRbIVhJr8AZVssFoFW9U5Zo4sUBa0shZYzBnj4LVp97XW9pkfkfomjnufBlParGt3UtfXv1KAqiiB8259raVaRlAUCZhZ0jqIqe3t7YQ2/N0Se7r69vQ1lgCeTSQCjRNssqdKM5Tz6M/WZH0c3Nzeh4tve3l5gKXTXw1arZW/fvg1lZtHFer2eSRycTqeZEt7s2KlFj5QN0bXZypTw/NREUGZBxz6OwwMGr/+x5+YYfaecn0pE88dwHHPjL1XyAi3+LgJFRUA/dS0vjCOtZEnBnmazaePx2IbDYQjwms2mvXr1Kix93d/ft06nYwcHB5lpAnRvMpmEKazhcBj0GzsOGNVcsVhgludEtU/8s6ZYpzIBR+r/Mrb1KQDpvcBAmQaWcRp5zpTOV2ev0b86R83wBgyoMvIbQ4fB2tvby0R01Wo1LMdCIXSlglnWWMQcrT6XfpY3mLh+TIH5vQlC9AZyE5SbZ0xix6j4yM6vGX8OeYjBpP2gtKSCMqWVdQUKuqJVzwCIOBt1mGrQYgBO2xSj2fV4Vs2Q4EUpVWoJ0AZAtIIeZTlYPqbPR8KY2d2yLQy/ry6n+95DHdfr9Qwg12puMScGyI29Fz1el7npcanjY0afd8Tc9pcmeSBA/38MR6OBnpkFEMl0Ffb45uYmVMKkn7HZfIbtpS4MY03Bp5+CUlCi4+8+8pCAsGwAcp9rbypbLy0so0ix72IO0ycIxTawwPnqPBFLC/0UAcYMJdCEQvIM1KkDCsi+hjXQvAKUyisirAN0rBq92HRDDKX6eSllCYocd8pBmd0tv1RRYJFaK18EHpTh8G2h7VB+jUYjRNBfguQxBToXCvVOWVZAqu7spkmv6/U6zHWiDzADylxxbXWQ0KDkGnhWSd8FDloZN3QYVozn4J6dTiesNDg4OLCDg4NQb0ATCJku4P4YdTOzbrdr6/U6sxMd/ei3E9c2EM1dXFzYYrGw9+/f23g8DnPDOnZ0nPhVPH58aV6CHzex+WXYP94jhXSOj4/t9evXn23K81KkTBBgln3mokg0L0BIBW6p9mDLJ5OJ1Wq1kIOyt7cXihQNBgNrNpv2+vVrOzg4CFUzze52hO12u1apVEJZ5Pl8br1eL7AGNzc3oSASINePIb+kMBa0+X6IfRez5UXibWheUFhG8piFbeVJNNw7FVVUT02nzuFvn5nPNVJJQQxwgAPnQUMCMG5ubsL+2/xuNpsZA6yDAaPOnJhmTAM81CD7AhcaTSowMfucivJ9k9dHqWNS1yoCGDEgkDrf922z2Qx0OoP5JUvRlIFOO6Evfjmr2V0hFqaxdCWFVsMj25pMaqamSOAjclJ9om0pg+T1iu9pNwwBGxuxGQvz/pVKJQA42g+o8WMoFqWzIgEBnGtbdckYgGC9Xtt0OrXhcBi+I+FMdbHICKb6Q79LAWJlF1utljWbzZAX8ZxgIPZMZZzxNtfWe/jv/Vgvey8idA3icPLksZAsiD6RG4a+8T86oDlhGpzp+45VDYQlSwGBImD0EAzDQzEUefZqm3vcewvjvONiTsVT69rZ3sDxsqlURRITg9YPUNAnEanS09yLKQSdToAOJRmKuVSdf8Jg+TbSPk1SVIaAKA/2ABTr8wJ8f5l9njxYNO2g9DzfpZxGKrL392ZAkkimv5m6oa+1yhj0Ko6FCPolijcOKSfDuxwOh1apfNoEazqdBh1iM6BGo5HZyMXMMpueQMVzXUClJrbisFV3YhnV6J5G0TFgrCBCE/tI8mL+9fT0NKwsoH0k6h4dHQUAwdjQol5q9DHAGG79rX0KGO/1enZ9fW1v3ryx2Wxm/X7fBoOBzedzGwwGmXGlYwfj70sOx6YRGRf0PyuSYK729vYCK9Jut0O0+rvf/e7ZwIC3n94R6HEx4B6bUvE2uMgOqRQxA3nPoZE5+gFQxp4vFouwqyZgjEJGsG4wVthtkg753DMDyvpqXoGvm6HPh8RYpIeWIkDij4uBMm/DtmEfHkTDi1CiKps3VCnWwCwbiZH4ZJbdMAVjYmahljoGGSPNoMCJ0Xk4rIODg4wysZxFo0AMsp+b4v7r9TqwCExlQPlibDHcZnEKX59b+zbGFOS9C/qHKRKvLN6IxK6h16Kf1OFjRFnVwZa5gDFockAD0zgvSdSwlQEEzI1Pp9MALFklwDMrACP60TwBPm+1WpmoGX2bTqcZdkDXWMNUkVHNNAPAVSn1mDC21KESgbM/AUBDN5rh3Xe7XavX68Ggcp5ej3HK9/Qn4EmjPW0XxWmOjo7CtMHl5aVNJhNrtVqZHRV1mgQdBZCa3QFYzTfSeiSqr9DSR0dH1mg07OTkJNDUzWbTTk9P7Ve/+tWz5Qz4seoZn9gxZne6Tbs5T9lKn5hZxnFsG9UqQ0UbyA1AN1imy34HZmaHh4fBjut2yQBrpquYxgKoMn7QVVbyMFWBrU6NGbUN943k8yRmE2P2R21yih2LHfOkYCCPqos5/VhN8RhAwFAR8QyHw4wSKM2PkTb7pPzT6TQ4ItZRYyygZNXxMmD0Okp349AwdtC1nE8CIoON/1utVmgf81uDwSAYdFVczTnwfeqVNc+xpwyFggqNlPQH5K0OXJd3qmMg+ct/BxhQhgYw8dLAgJcYIIgNTGWPmC4gMYpopN1uB10jAsIZ6v2g4AGWGEMSp0jG41oATAwdhYDYTlULG2nyn1ncsHFvADfzucvl0rrdrjUajbCzIfkIyrr5/AR0VQGKjqsUO8CY1DF6eHhos9nMTk9Pw4oFzfFRQ6796H80n0OBW7fbDc/Y6/WsXq/b4eFhYHZgCtDn55DUmPH6qe+VMavF2tQ5KAhQcKjOMcVScg2VmL334m2S6oxS/arX6/U6MLLYHZ6P94muAAZIjFWGkuTY29vbUAKZOhnUz9BcGH2WVB/E+qGsFNnBvOvyLmM/gF7sNmC9bDs3KkeM8hU1OhX1m2XXDqMUGC1/vJ+b5WXrHKwqFdfWxDUGsmYuN5vNTEIiz6WMAx3LNUmQUkTpo3yUk2tjLLm2JnFdXV3ZcDi06XRq/X4/GE6/zBJjF1tmVvQuPEXraX7NGqcQSL1eD9QcBlHrO+j+3urYfORVrVat0+kEluYl5wykmIDU5zhknlkjU7YN7nQ6tlgsAntA3oTOqQOqAAXosO53gPNbrVa2v7+fSdBjjp367ho9K1WqkV+KEuW6VH47ODjIJBdqYi4AQK/HOKI/dJqN5bk6PaY/Pt8CfSV3x5+v68s9+6BVST07gC1ot9shQRJgoN8pg9Fut63T6WR0/SnFU8L+u5jT0rHM0lcVb1v8clCAZExiQMDrgj8u5g/03XMv8ldg3larVQiieD+8S+w7egGzywZdgHBWKtA2ArD5fG6j0cgWi4VdXFzYcrkMRcFiAVmMok/1iZdtnL9nLflMgzllu0nYRpebzWYm/6aMbMQMFM1D5ClA6jh/fX8fBQxKAyLqlBUBQ9crNYShIFHK7I6eVRoThdM5N9rMNXSFAoMJ5dT5Uv98IFzK2QJu1HHoCgYfcXFNbYdG/Co6b8rz4GjUuacien6UHdGcDUWi+hmGqNvthmuxM9lzSdFUVh4TkBJvUKEpEdA5jnS9/rQrINE6QECdoQIFZdEAmaqj9C20/mKxsGazGQwihpbxwrvEWPiS3oBsnZpDJ7mWjk0PoPnN54BjlvliaFWnveFVMKA6Trtub2+t1WqF9jHvy73pH9VNnlsLlsEcAoQ9GKAN5L08FxiIAbe8aFWnTciFALylxOeQ8BMDGv4z3ot/n9xPf+u5Gs3qklb/zjWhGx1lukCPw6bpPdARzZNRwLherwOrALgHYDLdBVBJsQKPBQQ4T4M5D3Y1hwLgqmBgtVpZv98vrbsbgYFY9J73ULFj9BqelvUUIg6deVNFRql2cS3/Q0TLnODR0VFwyHxHRHx0dBQMLciS7Ot2u22Vyqfyre12O9DCKtC4WnoW5SKx7PT01MwsUGLL5dL6/b4tFgu7uroKKJXojvNRABxts9kM6J/vEIygMhSe4lWDrvQb19NkS80HILpqt9vW7Xat2Wza4eGh7e3tWa/XC5EwhvclJxDGJAYO0CUPVBWQYlTY771er9vV1VWYo+adEZUyJ0q/4qRZeoVBWq/X4X/mszHE19fXdn5+bovFwi4vL208HttoNLKrq6ugQ2YWHB+OkHfMvXCCJFeRR9DpdAJg1GJAyhgxLmC4NEnS7C4BUyNQIjQFCKqTagi5hll2iaJWnzO72yoXMNDpdOzk5CSaQ6A6rkWkcDYagT03q0V7FDwpWNRjGIvkQCiTozZRgRPvXVd4KBtj9nnOidL8eqwGSL66qzIC6FOn0wmMJHYXYIodBJhRlMjszl4xJnnf6AVljNE5jbKxySQXkqdCOWRKcM9ms8/8XsoHltGRooBE9U5tuq8KqiAXIEs/tttta7Va9v79exsMBkmWx8vW5YjzHiwv8lelTLEMMUrG3zPvu9j1mLfl5apRo3OZjiAiiuUy8KK0hCuDQttNlGxmmQGlUbxG7BhKqNV6vR72o4dqNbMoGKCcp0aN+lw64H3kpMdjdNVR8KPsAQPYg4Gjo6Pg9HQdsfbVc4hSbip5+hJjCfLa7yN4jieq1qWCOEK+Y4pAnZDqHvdFnzy4Wi6XYffB6+vr8B65F05Z2SCNrtB9dbpq8GknlDxTdhhgWA/VJfTR7K4AEGOFz+hfnSr0EVEs+ZS+1kqjTHNo1jnsFEsDVc/V4CqbBRjj/cWCj6cU7ZO8z1T0vQL8PBhQepn+QndZtaXvxUf/Oh3KMTrFoFO8JDKrTqveeWZAGU2dzsU+a66I9oXadN4rtpdr0T/osAIaWDwAEd9zT2U8ykoe0+j9nDK9tJ93pPZX8+GU1WIastPpWKvVsslkksm1KJJ7FR3axJiqQfZzSzqn4QegvlztvBhd5Y2nXof7gPoGg4F9+PAhoE3mqhqNRliCQiEWpYvMLBg/RaUMEFgDMsnNLCBUXX6IUuFQ1+u1nZ6e2nq9tr/+678OjAOJaj4RBKUg2YnonfYx2DHI/j3o3D+DEdRJUhVgo9lshkxylEtZB81D0MjLA44vVVIDWgGUj550+kn3ewfd09cwLAcHB4GV6na7IeeA/jO7e6/opDI8GLHj42O7uflUopWIZzKZRPMGYvkDaozMLOS5sIoA3ac96ODx8fFnS8B8ciTTDMpSsYsdjAL6jpDAqEwWjh0Gg0jWzEKJW2VgmPNPOXYcU8zO0Gf6/1OK2jS1NTEDr4wBdoFpDz73zsYsu9qDfgZs4uDJyle9wSZq7hRROEtf0T3+18AJPSHHqNfrhb8V0PGuZ7NZaDPBndcntTnkldzc3GQ227q+vs7YRXbuPDg4CCu/+v2+zWYzu7i4CM+hS819omXZwNgzTkrzY8c1+sfeKiMLEOAz/NfR0VFYHdftdu2Pf/yj9fv9AOKLZOtpgiJRQBC7BteJJZ3oYNS5Ev1OUZq/lj9fFRi6CAOtla6IwqfTaYhI+B4kqajYPxvtUbRWr9dD8RSMHIMCOlXbynVIlGIQMqgURICk/by+BwMxRKvzb0TvRPSstWYqhLrhKKo6odg734Y+e0xJRfqbXqPoc8+e0ee8dyIP5vb39vZCrsjt7W34DPE5GRpNc2/eAb+pEdDpdKzb7WZ0RylbpXQ1utNn4l66u6eCEQUDOF5AJucrsFDAYWbhXAUDPpkYndP5ZO6jOQ3ck+x/dJfzU1F+TB+8831O2ZR9RdTha8SsoMr3BTlC7Xbb9vf3zezOwQ+Hw6A/ZhZ9V9gnpqZwurreH71TtlLpbsAAOsLfmpujLJvPi8KeEdSxpbjmrGC7NTDiN9euVCohN4WpXgVguupAbWuZ96JtRT+VnaIflKlClzVAY0qFaVmmwA8PD21/f9/6/b71er1MHlOebMUMeIdextjT+Rox++89OlcEpedxbx+NoZyxiIfPQKaIp7npfNDwYDDIRL8kZmG0eYkaEeuaWLNPSnZ4eGi3t7ehpgFLC6GkFCnTvk6nk1ny4qctlJJVhAkoODw8DAU8tC28BwysJgfqVIDWCYDmTzkmfY+xd/tTkJTjiIkHombZJCqMC1NBOLbr6+vADIzH48x8/v7+fjBuAAyib4R3Q2SnS2k1kQqDiKFVXYQm9tMHAAI1ZDhhNpBZLpfBiGnkogBSQa0CaLNPhY00+bfT6YRlfxhpoiOO031EyG/QwmNqxH2AUgQOnlt3Y8yn/84fp3PdbApEH+BI1IaZfQ7kFTyQtOnHPcyAggsoeH60lLbubGmWdYzVajXoN8+hTha7pfP/XENzDHgmImtyvsjvmkwmgR1QtkLBOJH4fD63brcbVn8BrEkw1OJyMX+YCmrxJ5rQymdE/QAapoBx+LAn+C3N0YIVAwj3+3179erVZzltKXmQokMxBkA7g7+honRwKm2l33kKu9VqBWXwSAwHT2axrhf17dTlTWYWqDQQFcsOAQMYU+hMrgtdytIrjDbGValYjKM6iOFwaOPxOCRqVSp3Kw1iVCXtVxraz8F5uunt27f21VdfZQZcjIpSOpd3ooNbFd3Tk2UBwZcs/hnV8Hp2Ss+JgQEzyxhEs099qrsKdrtda7fbNp1Ordls2vHxcXhfTAfoKgHux7XM7vID1utPFQRjjA3j6/b2bmtv6FHNN+HaGD2dmgAMoMNsfKTZ7GYWjBWRos5Fq10gGZad6YhSfVIgbIpSwRh9HJ7PUykT5T83E+DFs6ax7/R/bBzr83Fe6CO2EvClrI0yqwqi1ut1sF+8B+6lAYLmSemUBqwU+VjQ1j6oISjjvTJO0Dkzs/F4HHa6hBnQZYcKfmu1WgCitJcdMyeTSVjSPR6PzewOcDSbzVAR8/Dw0K6vP23XvVwuw/11XxGeDfHBrAa6jGPYNPUNjAGAANOzjUYjJITqNCJ5QpqwzbX39vbs/fv3dnx8HPLNimSrpYXqIGJUPALK8XNynma+vb3NvFTmwz2VhMKaWYhgYoqnf2N0tEIb9+O6dKJmIqMc/OjzMofFIOMYKCbNvI5NbzAYoX7U8avj5hox0Xk6T/ESFUE3qVLq/bSfYuBD33tKioxnCii+BClDA6eAQJH4yCb2vf7NO2RLY/SbUsAYDCoPslR2PB5n5h9j0ze+zRqNQ9PrlIHqDtdkugBwq2vx9VnVHjBGhsNhMNB+JQPjWqdEuAZGTceMd1I+OPDvLiYxAPdSdVQlxcR69gAwwLJO+tXMMrrj5+fN7uyq1tEwyy5tRaD7tV2qh9o2rcqqDIHaVCJerXMQYyrQR7WB6CcgAMerCdAahdNOpjJU/7k/UbyujCBa1+qfAC/aqwEtbSbggi3TKQC/tJsEQC3HzEqtg4ODELzqtIJeB1CGTytrszYuOsTL99SkRuQ6JfDq1avMHDqiL34+n2eokJOTk88icToaQzGZTGwymQREqghVAQOlXFliooyAzrXz0vSZNGrWF40SMejIMcB4vXnzJtA5vV4vk0jD/TG2Nzc31u12w9QBAw6wwHIyj8L5gVngGc0sJJCcnJyEaYJYhKFGNDZN499/kcQilU3OfypRB18GECBlojP9vGyxDxLnKpVKmIaCsWo2m6Eq4Hg8DvQgy6vMLBgQfmtkwXs1u4sCcaBEHmYWInaSqcg5QNBPBeAKrpU10NwaIq9qtRqinNPT00BpMjfNNf00FDk86/U6PJeORxyLRrX8eOFd+yk3PcdPK7wUidHQHgQAjhaLhQ0Gg5D06fNOcEa+yBj7bCA4T34r06OMpM6569JYBQX0t+7BQZVB7tXr9YIt1KhfgxgSwFkCqMBBp94oY03ewOnpaXCiMCRMRbErJwm3ABYFm7RL9zUADMBUqA4qCPOMrAaKfEb0j8/o9XqBoWCKsF6/W8UVy/3wQSXjywcHKSkNBjz61oHkhUGlDlKpHLM7Z6hLjegUdYIa6asAPDRhDkPBPUB9UEea9EGHavSuA45IRL+LGQs1+moAFX0TTSlzQRtpAzQRx3oaViN7VQSzu524aKtGWSB5n2/A9dTw6lRBHj2pz67i++WliAclZSP9bUHDtqL67mtLNJvNYKwwgBqJ4YiJkMgt0HwXPZZn0v+VMiaC57mZnlADpMaS6EznlHVaRKMxZfzUfnjmEFCRovo1wle2UkGKP8cziHpv/f2liAcE/K3vA+fH3+R1wJCa3a0MUZq/VquFY7GhPh9LmSNyn6D49X36qTK+02Q+/6O2XaN9gKffC0ZBEvQ9UxJaq0DZC2U7YA+4v+qRlheHNQCsmt3l09Cn2HCm68jBwR/ynYIywMD+/n6YMmEVFzUEAMRlQCs+QdmcPCkNBnRpHR2lg4/PdZ6/2+2GzHQt8KOOyi8LYl7k4OAg3E8HL0oOaFBjp+uoY0ZLowI/P7ZarWwymWTmgJh/0QHAdWMABSW7uroKmZyTycS63a69efMmMBqat6AGmiRJvlM6lbkvPqPvAVC6ax0DmmVZDA41jjogPBOgdGMKjPn5ao0C/PVemoEtA3RUeN8PCQi889HoDkPHWufpdGrVatUuLy8DMICuPDo6yrBbamh0++h6vR7mGNUgeVZPIzs/586UggIFBRBmd3RybOzxGe3BcfiIRgEv4NjMQpEYHYOcp3PGumqHNnlRRxmb430pksdw6I8eC9taqVRCsTR9LrV76IxGsTgdnUrAicJK6TJSEjxhI2KCvpAPQ86Lji2l6nkev0KqWv2UV7JcLsMus0ytaUIgf+MQSYp99eqVHR4eftZv9Xo97Mqpq7govNXr9axSqXyW+8D9YTuur68zOSvdbjcwHPioWq0WcmHIR/MO3z+3jgntN95n7HelclcavoxsVXQodmNubmYZJ6NZ6UqNqMPW4is6T6/30vWpGml79KVgRBEl19FqWYpeSWpS40bb1RlyvVikrdQmiJMXqyg5FX3rXBjzcbSDH55VV0UoeNAlMPQz3ytCV6Xy7VEAoOCJ59Rj1VhtO73wFLIpAEjJYwCCmKAH6lzRd5YjrlarYLSJytB5IhjNrgZsohepSNrrqXf4WqIXAMhvv9eHz+OB6fAGzQMCP17U8fllXqrD9B3jR1kLD1D13p6l+BLEswGxv/WdKohQ8fPxmj+lBae0PwmSNAlZQZq/D7aZtsSO8ayN6pFOcSCstjK7S25VQARbwTScBwkI755nUJ2hzboCg+cHDOA/6ENYb2h+ZQG4hyYAHhwcZKZY8I3qvxSw0lf+d0wHuE4ZKQ0GdN5BaZSiQaRGgJfDlpLQT/5BoEj1oThO14ySsEf7dGmdUin6cjGwgAFWAfAZyO729m6tthoqXopnRng2nC7fUewCBTo5OclcU2lW7QuzuwEMy8KcHcafKIgtXpnPQqlUkTD+PqFSExD9FIa+W34r46I/UMGqIy9NXhIQ2OQ6GHQSVhkf8/nchsNhJvkI3ddlSYPBIOQcMI/Y7XZDjgrXJo+AMUTyIu2lzQq0dYrOgwjarmWIY9Ng6vyVdUD3Y0lQXAdQxGc+wo/psoJgb3RjehsDvk8tMebO2x/9jr8RpdE9KNBIHIdG7goMDu+XpYpEstVqNcxnayKit+n0M/P2WuyNpaJsY60JkOTG0Gb1H8fHx5k+GQ6HIYeLfAT6hqXcAJt2u229Xi/TNuwjn+m0FvkuyljRVgoSvX//3iaTSaY+y+HhYYa9Rvd0f5CYrvpATQG1stz6nf/RYmNlpDQY8Eh8G1Gn7n9U6GTOUSemmZ04XxRWHZbSSupofbSLIbu9vQ20KO1hv3N9iQoG9KVQ5UrZBQwgtFKr1bLj4+OgVFwHYIKD9n2GkWQNKWCAnAjoZBISFVnyvlB2BUe6f4L2uYIQjRq8MVVl9Uj7pywPBQrKOhkFyWZ3WdxEO9VqNegFCUaqCxhBIjnVf9V59FEZPe5HO4g21JkCxPU3xwMSNfOb8cqzo196PQTdVxug32nSsLJlMRCg//uIsyh68szCc0sqso6BAv43yzJ7HgwgupGbTiUAEmADqEzJNJbPsTK7m6LhOlrR9NWrV5mpKI7V1Qywt+gE7xnwgM6ZfWK/qNuhoGK9vltpgK6s158qD6ptjIEXfqhdwUot5ObmJlQLpcosSb7M/yuwMLMArmJAQPVX2Rz6RhNmdRrXB923t7dhqX1ZW7VxAmGKalIlVGfDHLlG6RglElJQFtA+CqfX9RQ0czY4Ro7H+PlsVm2nOjpFeX5aAeVWA6KGTNu1Xn8qZxkDPCgzL585TQaOKrvZHaUGyFDF4Yd2m91l0fIZ7VHgA9DQjZM8PcyzeSPpaTpVVO2Dl0q1PlQ0/1KuFzMYmhPCOza7i+KZN63VamH1TmxagOkF3eDIR5o6nYeuEuXwv9dR2quGkWkxdJXvGTPYBIT/PQDV8axjmXHgbYAHA0XM5kO/703EM3X67v2YiwEBD+T8Md7p6LNqv+h7wJ7oTn8+zwPRxD+ddtW5drO7uhhcV+07uQv6fnmv2FCCIYDwwcFBWNbqV8JQ1ngwGAQGTRO86R/8E2BbnTz9BhO7WCys3+9n6hwAIpSp1ildxpsfE/QxuR8aBGuRJD9V7d8bjGEqj8PLVmDAR0beWGCIQEskVigYoJNU0UGY/p6eBkUBl8tlKNQCKFCnqQpKsqFWB+SF6hy9nu8rCWJ0OA5lVINolt0W2SNxIvEYhYlCsKxkPp+H+WF9FhSmUqmEAcn5ftAy9UE5UAYgbIle09d2iEVQsVwM2uJR7kuSh4zky4jvg23u74187DP0i2k1dAv9Q4fRBXWys9nMxuNxZlxALw6Hw7C0UA2OXlNXCen45HqABi1YY2aZrHUFrTyPjkXuGdMv/U0w4PNt9D2kjOZL1Fckpjf+3Xub7AMzpb/5TEXn+tUZ6v08Na0Azi/tRl+wobVaLRSc4j1hi0jW4zyWkRIwYtOZSsBWEdyovlF9El2bTqd2cXERKgiy5BL6HPr/5OQkU7aYe7CPTLVaDev7WbatALvb7dp8Prerq6sQwBEAs/cILAaMrrfnfpqFYHo6nWaCOb8sFtHxoeCCKfAysnU5Yn6nBhLGhyQLs+w6ZyhMfaBUskOtVssUwQCR6dJBDJ53Vjgx0CAZ1pqoQSSv21yi3LofvUYgCgr8PRkIZneGRtGsRiT0BWBCFQOEqdSbN8yqALSHY25ubgIi9ggzdg0/rRCLoLRfU/TrS5YYvZp3rP72f6fkoYBA7Doa8cUcnX8niB9XHoiqPmKAmTf211Dd0MQyBQMA9VqtFqYp1Fn44jXKaPln9G3V9nsblAcafF/q3y9dbxEdZ4xFz1aq3vpcKz+/bHbHCKaizNjYVuDg8y34rfYR4Khz5ZRx99Ex19VI2Oxubw8iaqJwroED1eV3zPVj36mnQHs0INXoHDtKXkSMmeU3DAJgh37n3WjAqPZUbXalcrdMUZl17HYst07fUey3BnhlZGMwkJoeUOFlgqw0KvBzfDpXrfPT2vGq+CihDgS/xMVnharBI+r2S2TMLNPhZhbm+5vNZkgEAel5xfEARF+cZoRDMSEg5FarZa9fv87UVIeyVUSumbD6XMwX6/r06XSamQ7Qd6Pshv6k+h+F0kHxpRhQZBunfF9GYdPz9d0qq2T2eaKQHu/BnUYcKWBH1O6ZAZ1S8kbLA3otq2xmmfspm1Sr1cKSQqIspXkZHxqBeho7Bgo4T4FKUd+mou2YPr80gMv4xJlikzwVrAGBOlVdoaIFpFSUrk6Bfn1HiGcVsEns+0JCttbUV7sZc1ws7RsOhyHIQqf8On2Ssw8ODsJ26m/evAmOFjaMZGud3jCzTAVa/ApFlnQMqN0EcFAYiD0hAC4sgecYfIr2MXpPmePZbBYSKH09EQ8o9J3o+2J8PwozkDeQUscoNamZj1DzXil9lKsPrHMpGmHXarXAMpjdzbeD1rivIko9X9c5+2gBY1etVkNSDfQPz6HTJ7FIRmkfnhE2QvuMNvq5I/rOz/15w+gRp08yib0rHxXq5zEpizC/RCly2ps49YeYkthWYuPHO+fY35os6h1tTNe8jqgz51iliwHI2s7YtbVNMd3UY8syPKl++pIkRgnrfHpsXhgbhnPCHmoekV5Tr4390SBMjzO7S2hVe6+2hpVa0OQUkwMgQLdjVzSI40eZTE0C5560gURG2swKFPTP56DVarXwnbeFGhh5QO7fh+owIELHnw+4/NSXBotMIesmSAp2dfpL35sfOz5ILKvrGy0t9C+iSHhA/qbxZvElMP4h9TtEnaF3gorsNGrXtZy6wQnlN1mCpRsFmVmgaGq1Wih9zLEKXHzda8rIdrtd29/fDwPQzEKyCksjYSb03jpVUKlUPts6lDknBUpMkWh0pUbY96OnqlSp6V/eN99xXe8kvhRRo6ZS9Jk3xA/h7PP0m9+bTE/wDr3u1et3y1Ixwp661QQtpWx1/GBQlSHQqEopY6VPYR/I/ua3MmSqUzgP7Sc1nPoZ4+pLZarKiNcBZQXYZfXo6Oiz6quc5+eY1bGqXTHLsg5Kn6fa5ZkqpfR5/2yww/a6vV4v7E5INj8OnXdPm+bzedhMSEv+Yr+1umClUgn5Ve122z58+GC9Xi+wrpT1VfDA/RUEr9fr4BPQK6J2nm8+n4egU1kX3ovqso41HUe6zfNoNAr+SPfQ8WMtL1CO+UNdrllGNt6oKGYMU8bSU+X+WH/tmPHTa+SdW6lUQiKKdgZLrNgNDQNCLoNPCNSIXBOuYAS0tgFUDCCEuVYGEdWfmNYw+5xm9w5Zn0eduTp1FEXFH6s/eWAg5dRj71R/x97ll2CMtY0pfSpyvKnn36YNeeCiTDv8D85by5/yt+7H4UEnIAAHrkya1h5QfY2BAR1PzKFC42IcmTv29qFSqYT7p57X0/nbsANfgp6qxFgBgp9Wq5VJnIudG7OhCgb03SkYUJukwQjiwQCJ05xHvf29vU8b7FBoBzDAOn+cOiV4uSfOkSiZOiq6zE51h2lRIuzVahW2jOe6tBld8+yUWXYKFYYZ2l4jedVdzZlpNBqZZHazLEjmPkzpktfFb2//va3OYwL8b12xVyQblyNWhUoNKqVWPK2tzIJ3/v5Yfw99cTGj4ZPbQIn8ZuBoModPAFTkpdE3IEJBAv3CAGCOdTKZ2N7enl1eXtrHjx8DewBg0P5BkVj+gsKpMfWsivYFikufaPIL1/D5Gdr/OgB0wPsBgtHnPAUkX4JxLcsKbBKNl72vB3lK//E5fRmj8VODPWYcqG8OMwArRilWnLCZBfq20WiE+hdKOfOOdayoofQrBfS3tt/PCWt+grIC2u8xnVLGi/dZxKB8CbqZJzFbaZbVE3Xeqkf6W8exVkTl2vqjNLnXPb0m11VbqKKskpYzhr0iV0vn7FmFQiBXrX4qEsROgZr7oDshaoQOaMDWtVotm0wmwQcoINYx6POndJyRbMixJGXr/Xu9nrXbbbu6ugoJhQSR1GSADZjNZgHsUPwL3Y7d3/e3/qTeFSssytqx0mAgFtn76NQ3NjZQ/SDWYxTBpEAAD0rxCm2T7xCiIhJBUEAUVI2WRuyeitG5MLKnEagi2s/LAZURGb158+az1Qk6P7ZarULxH3W8OGnvzGKDFANLuxuNRqimqMWPdN5Q+z0Gxvy99F3GQMNLlLzoWyXFTN1XVNf8tX0U4OcbYxShzj1yfX5I1AL4tlqtQCe/efMmJBStVqsABlqtlr19+zaMEzKzMaa0F9YL0MnOiiRK+ZwDjZz0On5pIVFlkXh7EbMhsfd1H930Y+6pJQWMzD7PfvesDQBO7QRTkgRIlUolvB+caAycKlvknU9sNULKznN/td/YLaaw1F4r4+ptF3+zXJqaArqzIDVrVquVdbtdOz4+DlMGjCF9Vu+I+Rw7qmBEC+GZWdg/hiWB5G7h8CuVik0mk7CckmWDPq+NAJV+5LfXgxgToJ/jex4cDKQcRMrhI7FcAXX66mBQKh+hq/CZL1vqUTGAAeU7OTkJG/doFTaiG5031XXUKKxm4fvozgMXXbqotKgmaaEEnlLVqm+qfDrI/ByXfwc65aHoUZ1RLDPdv1sEY6MKF0OoMSlikV6y3AcQxJC7DlKz7B4esfl43+fq9P075m/AACBYqdrDw0Nrt9shmxp9U+oS0eQtD3I9eI4FBbRX+1ApWnTX6zAGlj6gL7mGB/6eJVFAm6eXm7zHlyI8H46InCYy2JXl1E3bzLI5Ql53zLJ2mXfNeWZ3yc3oTAx46HvEvqXKn9fr9UzytvYz5/MDW6Fjg+MAEVT+05UCOvdOyWCCO6asNNjCqWstAK9n6J9OFdDH9KtuIAZzpqvBGFc+wIqxASl764PEmA/eNLdrYzDAzbVzPPL3L1UfShWOh1L6sVq92zsbR60PqE4NBScZyS9N0emB169fB+deq30qSDGbzUJySa12VziFuatOpxOSTLwRQmEU2fEdSxZ14KEU9BP31+xaXSLEHJkyMgp6VGl0AJpZBsTQTyRlaXKWf0cxhkWNhRc1ujHJA4lfimzLDKiu+qiNqEjzTXR3OI3CYn2m85mcx2fHx8dh7wtAJtMFLF0djUY2mUwC8FUAqpE69CWgVYtfpYAE/aVGW5PWvANQ9kCBB3qqjsmLHwu+/79EfYuJB1CsPx+NRqHaKkEPc9ZmdzS9gkUfeGBT6HcNUPQ9KbOAnSKXxCcuEw1r4h8gQB2sT/jmXj4/ALbU2yXPfuBkx+OxDYfDUBXw5uYmUPeLxcImk4kdHx+HsaP6Qxs1gduzIThiWGKd/gKQE5Hz7CQgslpAHTX6rb89CPG/VTc8w6uSAuox2XppoRr31G89ziMrpWR8aUbobU+HmlmGjuQavFRVKo7F8KLAROgoLINDGQJoNJTBIzX+rtXuVljoM2p1RR2I2icKFPQ3lBn9qH3pnbMicJ3Dz1OAmIH0QE2fMc/RlzG2RYDhqcRHLl5i7Iin9X2/5r0fH/VjtACGPotfE/40qU/b4a+tBhkdY2qA4/2Ul0bysXeiz6rRm+oExhr9U2Opfecje50X1f5SwxeTPECm46FIx/JYKv+dZyFS5z22+OfScU/iHQyB2gKzu7wq3oOCUrUXek3O1URA7Qed1mQ+3LM/fJcKEjlWN0jzfUsbVR/RHWWnsLc6vvmeapwk58EI4Fti9lRBqfaH7yt9Lh+cYcN1bAAy6Ff6SfvGM9seBPAONSjkvejvbWVjMKAvSKN8fTlKh2OQ/K5m6pi1IpU6Z426uQ/bPmpkAjOgnaEGUw0zyVTn5+dmZiHZCjpV95Jerz/tK8/1iFgqlUqmrSmETCYsz6oDq1arhflZchsoyoJ4heKaujsi2ahElR4QeYnRqHnKlKKa8hRv22j6sYT+0OJPZtmpLqUUeZewKxync91cVyl/HcAaravRQl9hvzSyJ7pAX1gaGNuTnPv4JWBKt6uxVspV26vX076ITZEhzHWqseQaei1sgV+a6MsZm1lYF64UqoJtdUgYbX23arR55jzn78UfqzVG6CutmviUogGVrklnx8rlcpmZFoIRrdfrYaMc3TvCs1H6jNgaXaqo4z/PQep3PnjU929mIUcKm2ZmYUxQutdPJ6tvwW5iR9XWaz4B7CqrC/y0g9pkgC+sBPf1zIkCGO0zjsWHKEjq9XpBv33OAc+vQI4+9FOMamdS9tuPw7KyVTliHpAb6ovSl640DujNR0dEM/wmKgJNgX7paBKe/PKpVEf4RCvNWCWa8tSn3t8rvr58FNIzE5rk4s+j72iXvmyNpjhOARXUlFJDWsPAU1557y0mDAw/kO8jz80IpMRHlF6fNTLnR3fkM7OMrnI9nUtXMKBgTfXX6w56SbU26gPEovmYvmDIcJip/vcgaLVaBQPM35yLMeRHKVyMY97402vr9IdPktLzYgAn9nnsner3RfqnEaHZHfjVjXgAAc8BBmLPwHvz2fS0n791+pW+573xtwJRnUowy9r22P+xvlMnqc7Sg7VK5Y7WV9p8vf5UqlqZpxSjlwLfMZCvG7ThfPPsnGcAdCrVM7fqoHnGFBBV38Dz+xVf3E/HSgoM6D28bdDfZWTjjYpUeHEk5WG8dK5Una4au4ODg1AICIPolQh0rvQPUbxP0FMl1bkmv+0riSH1ej1UwEI5QKo4X0WfRDc6b6pUjbIYMafsBx7naLUpRZLeOfG50ksAD12b6geqRhW0LWVg8mjRIsPrv9PjnxsQ+MEb+47+gWZnv3N1WtqHyh54xksZIwWbRCIaGXc6najj04hf2ST0So2eOgbap7kA/vroNpur1Gq1kFDoAY9e26+vVsCvS9u8kWQDMpiwev2u0BcggXGuQQV6xLjSaTuzT7vPTSaTzPV0vPspDL2mmQWHz9y2PitzuzB2lUrFfv3rX2+vhFtKzCbQZ7A/UM9ksCtIjeWieLY0tvOk1pbwjq5er2cS4bBHfidU3mWlUgks8P7+fqbo0Hq9Dkv0BoNBsPEHBwcZ/Vebp+wFzAD2HD8Eq0YRI5LGNQ8L8XlsBKoKtvmtbC3PpmxsUQClOsh0Qgxw0B5tV0w8kI79LiulwYA37mpEGeTsTKWO38/xYwiPjo7s5OQkQxX6pUwKBlAuv3sVRpeXYmaB2ldk6ZGzIn4tYqEUIwOHJRq0X9EWfaFGy0cQen9/Du0BvevxsSjfK01MgWIGRGUTJdG2bnP+S5HYANNn4p1CsVIgBR2rVO7KQqtRVaOBo0fv0RnNGVDjgeHStrCWWiNzbSOOV40Tv9F1jZZUL/V5cSjsWqiJZUrPk/SkYECjfgCPd7w8p+qyWXZXQX1GPlf7oc+lAANhWRl9rX2Ao/LgRPWBZ1ssFjYajUKGviZ6tdvtXIblscWPZW2HFsExu1v2HHP0gDANYPhbK6fqNAPnx9ga+krZIZb+ARTM7paRksQKYNCpL+bzsWUAMrXpsRVVjMP5fB4YKK0HAAjSNjNvr6I2mPGlY5V78tuDI8/OITGdyXPsRaK6EJNtQQCy8TSBRiM4TDL2mXNXw4qjR4ggNCsV9OapTV6cOqMYhaLGT42QWbY+Agq7Xn/KBUBpUaZarRaAxGq1Cm0jA9UsC1h8dESbUHz6i+uBTM0sY1xhCMbjcWZ7S66vx+t8Fu2J1Rj3zECeFKFZva6ek6LSPABTI9zpdArv9dCSej7/HhXAdjodOzg4yMyfamYzPwBRfgADlATGEJvdRfaeivf0HjrlIzkFDGbZqF0Lr2juA2OK+7IMiwxnymwTqei4MssaL43YNdL24FaZC/pBI7dWq5WZx/W6wz4gOjWiek00zBiuVqthXOLM9R1PJpPMu+a8+Xxus9kslIZlfClj1263M0XGXoJo35tlQb/ObVcqdwXSPDuifyszACDUPA+zz/co0L5SUKJ/a76ImQVQvFwurdVqhbX3HqhzTXW0Oh1F29FXcqYo9tbv9+3i4iJsMKft8LbK7PP6AmrPFQykAiPapeyuvid9tk3eceyzInt+Hz3dOIFQlRCDxvI4XVIB+ut0OhkKnU4l+lLHp9XxzO4oTUVySkH5l6Vz9pxPxE2bqQBFRIATVgDDi4UOW61WwciMRqMMHaZ0FUsKT05OAt2srIcab6WLaN9wOMwsBSP64llA3QAa+gyDqP0W+73Je/bneSXTQc/75TPP8Oj5zwEGzNLzx0oREgmxpBRDgsOnrz37xfXNLBhUphkAA/p+0D90AEdtlk189VNs2teAQChWnX/V+uscX6lUwtatjMHZbGbj8djMLJTy1qietpPLA3hX3aVf1cCq86GgETU+6vVPVdjQYe7LdRVw+ekZ+pi15Ixl3s1qtbLxeBx2o6vX65k94YlcR6NRtPCL6iyAodPpZKqGPrX4MaxOSYGl7//U+Yi+I8310gQ9ZbO8nmokr1G7OmymKXiPvI/xeGyNRsP6/X6GXaIdmhSNfffOW9kPZTxgA/A9p6en1mq17M2bN5lEc9VdDSL1x08x8Xz6o/2sCYC8H6/D/r34d8LvlL16TFC68TSBj9D5UUOl33unoRmfDGASKPxyFF3Lr9mdGjEz8PWFMZA1uY52o9wgf3VcTElMp9MAJBqNRmauid9KyaqjJxKBasTY6dyTRmBa1pJnpCyxzoGaZfMSYk7Y7G75jzceRe+VY7mP3iMW/et8obaFd63KHJvueGrRARYbiPp86nB9lKXOlfO88U29F0TZHZwZ/axsEXrOsTgz5roBhjr+iHA9fUn71dDp/KnPaVFnoawUAF2NpU6FaFIvtTpgSxS0+P7S+yvTou+KPsGJE9nf3NwE2pktapUJZDzxvAAGzvdR7u3tbWaagJyHlyixfirzt1mWRVAnj855Z6hO1NuEFNhQW+8pd9qkjtU73dg0aIyZ8mwJ9pjqhK1Wy5bLZUgg53jVYc9Aqa/zf3sbq+Pe64q3PR44pd7rU8tWCYQokEaqIP5YR5jdzQORpLJYLMJ8JcyAOkWuy2coLpHZYrEIkTvHQDsSOWCkNJEGhdRNNYigoEx1n2u//MQbd01sIYGHKQUoN3YWazabofAL7RqPx8GA038KIBS9q6IqUGKuFWVWGto7Yn2PPIdXPIAUyVVa0UtpVK0FrpEC11WUDUL+1a9+VVblHlxigMCDWyJunIXS7jhjopkYra7Xmc/nmft7A1qr1TKUvxoUZb24HkVmptNpAKx6PXSIZEDal1rap6wFY4GlaFq8RhNyq9VqplQsy7pgDQ4ODqzdbmcSMHVu2AMpdIad57i/n0KBcVqtVnZ+fm6Xl5cBFAHYq9VPNeCXy2UorMQUn4IsmInlcmnT6TRMmZCDgBOZz+dh+rNSqdh//I//8dF0s0h8X5hlk4E1avdRa0wAnmZ3U6m8A72f/4mJtykI+s3UkK6g8m3gc2/nvBPW3AdNhKxUskmsvGds+WQysWazaaenp3Z8fBxqzyiQVd+l4Ej7XMebBqg+aFKw4c+LTSdwnTLswGPJxrsWxv7XqJ5Ih87Q+s36ez6fZ6goULwmoGCUNdIkUQSDuVqtQnSgoEDn3lnzqUZGr+FpIGULlL7lmfWF8fycq8qNYqtj9/0YG3AaIdJuNQB6X69kGpXqvcrQXBoB+giUJB+lo/Vd6bNzT79k6bkkL3Ly0Y4Ho4BQZQYqlbttpdV40ce+H2LMitldToxZHAx4Bg0wMJlMQglaz+Lg5IjOGVtcSw0xuqVt1ef1uq4AQgt6kUjJVILWlke4rva7BwX6LryDAQAr46bvaDKZWKVSCVE/+RD8Td/c3t4G9oAqizc3N4HN0/9xJvpun1O8c/DgNsV6+eNj14397d+/t3v+nLwo18/LK1jVaQpfIZV+Vzsc083YvTWC5z0yPrxN9dOsPnr3YCDWj6n+SUnMDm1y/ib3KHPN0mDAZ8grLaNLQ+bzeSYi9Bn7nNPv9zPzPP4BcGjMLXJ+v9/PRGMYCXWgOohBkyQxwRBohUHoYHIYeDauqS9N56eIwlEOohNWPBwfH4ed4nq9XgBH5CuYWWA6iKpgJmIbImnCmiYT8kP0Th1uLc7EXD0OTbf71H21MYg+GQjqVZGtp8w8mtbBwbP823/7b8uq3INLbBDzns0sOAplXoiszbLv3he8QhQI44B9xEY+gM4p8r1GsLwDAPF4PA4gWcE340QjDrLCm81mWMKrRa20LYwRBTOaWc15FOxi5zktbkNGujImGt0r0FGw6t8PCX3UVlCA1u/3bT6f2+XlZSgzy3N///33IbJHxwEAjId+v2/L5dIGg4FNJpPM9rHKQCogbLVagcF8LilyQJ4Z0PP8cf5zvtPfqXumgEEK/LPkE50hKZdVZ5pAXqvVrNfrZRLOCezU5uSBgNj0KHaacXJ5eWnz+dyOjo7C+EC/VW902i7Wl2bZjYLQ9dj3CnLVt6QYFe13/07KigfVZWpklAYDajR5KDoAugSHgXOLzdmbfZ4M4l8cxysFrUlRPopWJ4RB17l9NXj8b5ZVplrtrihK7Nr67NClXMMrCgre7Xbt6OgoGFQzCwZdHSrPzvpgnJD2tSqdp5/UyKL40M+crxEkwGE2m4WtNBeLhV1dXWWiJURBWcrg+PfgdeWliLZH3z9tx8FofwPMdJnszc3dZiuxqFH12CfGAtI8GDCzjLNEh0ly02Q3BctMD2hUz7mr1acNXLi2AhSd41QAo8/OlAg6zPpwBQM6Dac5B9oXsBMKsGMsleafeMaNXAHANMDIA1mtHMhUwHK5DGDg4uIiTAMow6AGWB2Df0fPKbHxpMyAHldGiqLZFHCIjSPVGXyA7pegP1R+5XjAKrpqdrclsrKkvk3qN2L94oM2xrbu3ul9D/dQvUz5rNiP3j/2f5loPY/pSb1bz/AoqHlQMJCaj7q+vg5lMX2tfzWSPLhG2t7R6TFKWfs5Fq7nKSSP6ukQ0KZWHuQauuzEU+lEOtp+2osCEznu7e2FUskHBwehyhwO4/Ly0m5vP19NgEEnt0Azb+kHjJXS1MPhMACL5XJpi8UizOMyb8rAq9VqdnV1ZavVyobDYXhnzI9iEPV6GrkBIDx9VmRwdDC8FJrVD0QPcjUS90lUPtHIz3F68UBDna2CUBXuq9MDREc++c5HMPps6Fq73Q7FXYj4cdoY5VarFXb1PDw8DDQ/qwi04BbvkYQwjCn3JiCAdfLshT4f45O+rNVqoagYtgFnPp/P7ezsLFD85B0R4aPzOH70eDqd2uXlZaglwBSDgiUAm9ldsKEBhLdlL0G8s4o5Qo6L/dbrxJyOt8ve4euKA19wSqeTWG7+9u1b29/ft6OjoxCVU/SNcXV4eBgYTN79cDgMDJbeI/ZMGmBxDV1xUKt9qndAdU/0WxPX/XnqYzzg8lNsGtzp//795NmLGKjQZ/TModoF/c0zodf/9J/+0+g9VTaCvAxeGgbtTZ4AczE4QbOsITSzjCGgA/wL1oGqTkijz9g8jxpGNZq8fDoK2hdj7msCIErLe6XQ5EAqar169SokC7bb7UyFM/baRgAlmhcxm80yS23USNHH9Nt8Pg9JTxi/wWAQkmUUDNA/Nzc3dn5+bvP53AaDQUieor9JZESR9Jlph58K8P1FP/r3+pw0K+KNhwI89Ev1ThkC/e2NRmpwewfCO1f91OspmtcBzTSYXjePjTC7i6oYl5rkZHan+4ABtjd+9epVMJS9Xi/TX5q7o8ZIAwWmMKbTacZRK8jy4B5mQcuB631gr/r9fsjs53mYVgDYMiUwHo9tMpnYaDSy9+/fh+/U/sQiN//eAP0vDQzEJPYsPlrku9jneq4GPar79Icm0aqNVKCMLWy1Wvbq1Svr9Xp2fHycAQNqK2JggPfMlIIH38q86QoyBZnV6l0FxoODg2CvdcmwD1z0x/cPbJGP9GmPZy64ZhELkwJ0el2eDUZMx5UWzFIAbvbAYCD24IhSrN4h8x3OwIOBWCfouR7tqTNSpKqUq+9EylNCTZHkpIoNkq3VamGeS2sh8PIxiJzDnNPe3l6YEmDpCv1gdjefphERBlWBDnQqSY9K667X6xAlwgx4x1WpVD6jSrk2865kpKNESjmjaJ7KpS9j1J0CgxRbkEeJPYXE7u+RvoICvvfo3oOB1Lyf3pP7YHT8NT0Y8BRfrP1+/HhArSAMHdf8EeZn+c340LGhhsfszqCh9+gmeSfr9Tronhby0fl4rqOiY5A2QGtCH1cqn2oWKMCeTqchYXA4HIapALb3hQkgB8bfX8eH72f/7p9byrRD25xquzp6BUQ++lf75pP8FAywkkSZI903hvyAw8PDMGWgjlbvqfbS7M5XaLtj+TaxqVz/vPo8tBNHyXH4EbPsNsJmdysGVHwwqmDZ24oYYEjZTb0WTDJjCHZNmTd8grffygyUkY1yBmgojlj/14eDRtLG6DIpP+UQU9oYnRX7XLNSUU5lJtbrdYh4Wq1W2Mdas1pBjTh1oqRer5dJdgEM6NwsCFcHhSoxzw/1yQtUBKvRIErud3HDoIH+Li8vbTweh3MrlYqNRiMz+7zOglLOo9Hos+kXfnu6CaOrg0S/85GrR+j+fT+3pAZnTHRA5gGcWMQY01110D4i9QDEH1MGSClDofeC+SJZqtVq2cHBgb169SroONMEmg/AeyQBz8wCrQqgwPAsFgu7uLgIy4V1nb+2R6uPatVRzU3Y398PxZpg4Ghbt9sNEf54PLZ+v29XV1c2n8/t4uIiM5VwdXVl/X4/6uyUcuY+asgVjL0U3TWL5wvEAIAPoBB1vCoEOt4mUgCNd8O7I2kWu9vr9axer4eIm6kpgIGCZmyvJpgqA6l70eh+G7SfIA198O9VA0OCLc7lHjhTVkjRr/gNBQw+slf/54GkLkOG3eLeMZvg/aY+N+NqNBrZ9fW1DQaDkM+lid8KAmKB8yb6u1VmjDdSKXpKjaA3dh4JpcQjdkVsKC5JTLrRBpH17e1toD1hBrQSlSomRgplMLMMmNEITJXOTyHEojoSFL1SqtFRit6DBV0xQHSkKy0UuXowwFSOgglNkIot/0TJQM8YzVidAX7rsyhD9NySGowxKrXMuXqNFGjVc/MASN440CijSBSgagRFpE/EjaFmZQBGX42TvjNlqWgT+8RjtNRI+bwGrqFto72pPuRz8hEUzCoTxu/5fG7j8TgkxMJOeNsR68sYK/DcLNYmkmKM/DP5xFA9H8enDCnTNjh62CIFAzCp5EqRVKqVV33bvKPCbmFD/ZjRz2PPqr7A5wf4aT3uy7kp9tovb/T3jiUbYr/Jy/HRP4BLgYPeX9tDgiwMF3UztOqmbnCn+/do32keRBnZaqMi31FeufRvj0J1DsbTrF6BFUzwcqEQmfMhyxlGAFDAyoHb21vrdDr2V3/1V9Zut+3k5MTa7fZnEXmj0bDT09PMRkjz+dzevXsXjGu9Xg/MQqfTyVClRP1EOrAI9A1Mie7hzd9md6WdWeOMw9WNVFAKorXZbJbpJxRMjTLPxwoBXTao94gpFdnWOoWgCqj3KHKMzy0p6s5TjLGoHZ3V8z07lorC0HHv9GMOyEcaiBrEWFvRJ7PsxjAnJye2v79vX331lbVaLXv9+rV1u13767/+a/vFL34RWB2zbBEulqUyTwtTwLTShw8fgsP1hZU8WNcETAXNStGq4QSo6vthSgCdH41GNhgMbDAY2Gg0sn6/b3/+859tOp0GPcfJab/RDjW8/l15h4PteW6AoM4zFox5dkCfmQhdp4zUpsL4YDe1ciQluTmm0+lklgTqVAAsLe9Rc6UIIvgeHVA94brKCJCkqOwkOkFxNlhUv+pDx5RPrsOh6hjgfn6/Ea6ltkOZcuwuyausiCB3hjbq+NbKmNhfxtR4PA7gdjAYZJa+KogAtHsA6BnhsnIvMBCLXMoOmpgR1O88oiUCJ2pXhSUrVacJGo1GUDoiolarFc7HINBpiihRPIysGvRY9O9pdtqq0ySxCMdHzURCUPMYaB8JQd/HjCbKpeVVidwYQB4MaOKgRoYKBnAEXFfzCmIONfauX4IUsVT+GP5OReibAJ48JsKPoU3ZCjX+5K1ofozmAzAeMILK+LBKwI9Hs7uVDiSvwlTp+PQsgLJpsedX5wWw1oiG+6HT/ABidY8B2Akdg/oMHuSl+j/v3TynqLPPE7WtSv1r7gjvjOlNon5YAbWtyr6yD42yBWxhz/X8Uk3arkED71nBtc9D0uM5FhCJjmCnCbrIddEAFdvs2QBtSyyy9n3tGQYCPmwxukkgqvZfk1410EK/ARMAqNFoZLPZzPr9fiYI80Agph/evpeVrXct9EiU72KiDsM3XBNBuA6IE8OFgVMUC0KlqIoWR9FkPaId3bWKyKfZbAaDslqtwhwSSn17e5vZ3hNkvV6vbTgchnX5usWrjyD5n/PUGZtZBnwASqggR59qYSAS//it98VhsxEL0RQKp79j2ereudNOBQgxetdHrt4ZpBzpc4k6eI2iiqQIRPhjPUjS++vfeffX63hnpgCA5FXyXQ4ODuzo6Mg6nY69efPGms1mYLXMPtW7wJCjV6vVKow9DDqRCoyVRia0ScewAgLfb7Hnur29tUajEcp1syqG67LyZTab2cXFhY1GIzs/P7d+v28//vij/elPfwrTBDc3N8HZKXujDseLTwLTd8JzKI39XFIEBHD8/OQ5emwaLBIOn2lWn0elCYTKMKSWXWpbSATVRGgtX06UzPvREujKpOq1VeeUudGcFPK3tN2wD5SvjkX7XAvdoc89C4qjXywWdn5+HthlXVHFSprb29uwIRjAgU3CqJGxWq3C6huALUGcBmGxhHNssyYd6vRwWdk6Z2CTiMjs870NeBB9mRxDZM+6VAUDzOtrtI8hpNIaL8TTQ3yGIuzt7WU+10I9KANKzwDi2dnkBApfFUWf08wyxpKX6ldAaH9AczLoFHnCDOicEU6bXAEopn6/b8PhMCgtgEfRrCpYUcKcPleMQov95M35PaWo89wECOix29zTSx5rkrqHTknErl+pVMK0FQCA5Nd2ux3yZIj2zCzsmsj/jBH0WI0dtSnU4focGrNsEmOsrdr/atDMLAB7vxIGo8l+DOQETCYTGw6HoYYADkXBjLc5/l3m6aR+l+r/pxYfgPmxSpt12Z8GT7pCSp05YEDrsPg9XTTLnv9VD7SNtEXzFPjMR+BExBp4AQZwml43lclVoW3dbjeAGNhi2AuO82NN2Smzz5dOq63UaYrpdGpnZ2d2c3MTAC19qVOwPK8GdVrzhfFF4OeTA/3z++BN2T3tPxiRMrIVGEhFRl5BNUpGabSKm9ldtrNf2gedTxlf3WQFhIcBIclFi/Z45+aToqiJwLwR6E3boU7t6uoqo/Qa3fsIUOe3tC3cW+eb9DOeTREe0wM+cUT3Ymeu6urqKhTq4HudAvCoXOkxlSLDlwcE/Gf++JcgMcdw32PLPF8sslcn6R1V0XXJqKbGRavVstPTU+t2u4EZgDnTpb2A3kqlEvQKYS4eZ0HSqUYnHujxPJq5DUOnz4pgqHRpI+wUoJV7AGqpM0DdgA8fPtjFxUUYv+q4/QqmTd7RS5UYZe3trSZC4/wJkDS5D5YTuwujCjjg3eocNzYCu8F8uNknlknpaWWV/BbRWggNOp3zF4uFtVqtoGuqc376SRMhdYqANmvNm2q1Gmy7Lp3Vzd1gLmC+1G/c3t5tgsf/o9EoVLL88OFDeO5OpxPyAcjt0mlWjf6piQHjplNvms+goEBXbuHodYpPk8OV2S0jW9fZ9A4/hlg9pYPioQw4TZx4r9cLdD8vzO+4xjVRal3aR5KGj3jV+fn8AH35LM1DqbgXL0OZBlB0o9GwTqeToZYwhHpvTfDC4TPAaA9ZulD5CgaI+lEUUClLrMbjsb1//97m87kNh8PMXgJ5lLVZfDWI/zsWgejfeSxB7HrPJZs6d++0ywKCvAg5BqZjjIHvP2WPzCyAAKYCWDoLo3Z6ehrYNDMLoNHsLj9FyxibWXC8uvTLs1cYTh89q+gYUONaqVRCVNXpdOz4+DiMQ5yIAvfJZPIZGHj37p199913YfdBtTWMK8Z3DOz6d/ES9LKMePvq57hxsFoIDdZUV0oRgWNbAQO8c53uNMsyI2pHcVCARZaVUtsBe2l2N3VAkEfwBjOwWq1sMBgE26hz45VKJUT4BGrqPzRhu1qthjwWnUoAuHLvTqdjJycnYfkkfoPpYX1G2qiO9vz83L799lubzWZ2dnYW3kGv17PpdGqtVisAJi2GRS6Abr/N82sROJ3GpY/1/vgFfILPK/CsQRnZGAykLhwzlOp0UUCifp2HAqX1er0Q7QAYcMqgOJTfRyUewWq79Dw6Sp08nck1NXo3y0YbfK9sh0b46kT4zOyOmqINOjhQRqWvGEiauIdSsa56MBjYeDwOTABzUChMEQigf/I+j0Wrvm+LotiXwAz49/IY19F+ijmZWD+qMY9dWx0dho1EWFYMaKU3VhB40L1eZ7OtV6tViFAw5OS/KJg2s4zum2X1PJbdrstQ9XnUyTPmdR269gMM2GQyCaD36urKhsNh2I1Q16SXYVL03cTeXezc59ZbxLc5NT4JnNARpa09rY9tIArX3CXNaVLRRDhsqeZcwQhQhIroVXVYpyBgBNBPf399L35awINStf06d6/jkWvDWjCmuPZqdbdFufoUTZ5FL7HFROYUvoKxxtET9TMVwDV8XgDgAFuv0b7mAegzxALVPNY3T7aeJvCGLya8cJaoEEWjoLpsZG9vz968eRPmtOhMlp/wskBE3NcbrVgyi1m22MTt7adlHKBBnZfxz6lGqlqthvXZDDizu2WBRP04fI9MFb2BkA8ODuzrr78O1JPmOFDEhRe/WCzsw4cPNh6P7c9//rN9+PAhZHYrhRR7T142SYiKOfwiZkCPeWninXUZgBAzupwbo3BTjt3sjs71OhdjDZRdY9nr69ev7fT0NETWrVbLvvrqK2u323Z6emq9Xi8KOFqtVogy1CCRtOedq3caCljX63V4BmXucDjoLJ+t1+vAQlDxkLldwAhjplarhT0FBoOBffz40YbDof3f//t/rd/v28ePH0OeDm312eNqCGM6G3svHlTcFzg+tHg9821UIIC9ZQpVc5R4RnRPI3/6TbPgOcY7Ri2EhjPSiF4dFf8DJmCENajS96h6X6lUAnuBf/DMlPYBoIJxpn2HTuDI6/W6jcfjkKPGXjR++21YWvKwLi4u7OzsLAN8vv/+ezOzkKSp0wSA7ljRIOw3LIIHA6mcAc8M8Zz6exPZqBxx6iaxgaM0jdJUWoOcqIDIXxEsBsQvS9JIVxXTU5gop865KxigfbFseu7l51ugM2lbKlJWSSEzaDodnB646BwQqx6Ikkio8sWHit6Vfu8dhv++6Jw8ZiAFHp5DvCPn3ZUBtEhMv/PO8d9tMzh9opbmyBwcHFin0wllXplaY3yp8dPxwPwl9QEAxBit2DPSdpbrEtlpPyo4UqNllt1AZr1eh0Q23UlNjzezwFroD9GYB72+vV7/vV6WfRcvFczmifY//6sT9ODTzDJROHZEc5w4hu+wSST36Z4T/Gj+gDozHD+21Cy7Nbjae50G0ARRmFWCsJQt5plUj+mLSqUSWAtlBbT9Zp/bYoA0y12VuteoHb8DyIVB4LeycLrSS5P/uKYyLH4spXRgG3mQvTk10cTszknoWufT09NAWxFFk0BiZmHOXLd39dQPnaKUidldAgtGgs7kGAUBSlvFnsPs89KuCA7czELhIkW4vCylu3S1gkZdvV7PTk5OzOzTPC1SqVRCpillV+fzeZg3/X//7//ZcDgM0RLnpN5DWclz5LHPipiC1LVfopQdPD5qjgGL1HW1f9RY+wgIg6SFtY6Pj63ZbIaiWG/fvrXXr19/Nu8JsPQgj2jk/Pw8ZOP7pEAtOqS6rEZ6f3/fGo1GSP7T5F+mATH4yn5gvBkXb9++tTdv3mTmPnXMMif7/fffW7/fDzr/448/2nA4DNdSJ4eo3dDnSenlS2QAYqL2I2Zrze6SQ6lcp/S7OmHshDoa+kuDLK5vducoNUDR870j1YRldWKqT7rsUMeATiWQ1Kjr983uihHxt592VhBjlg0itc80x4Ql6jDHAB511P1+3y4vL8OUlTp1X/tFg1CdCuB4X8jN96Nvd4zBism2Or0RM5B3Az/YPDOAYdOEQAY0lKPOP5pZRjnNLKN4fM9vEKo3LJpwoUqpbdZcAR0w3FOBgSa3YCz12TkeMODn7DmWeSlyFswsDA4UTEsPwwiMx+NQRwAa1s/rlXmX2tZNmIC83/78mE48t2wySGLRvR+Im0aaPtr2IEqBnSaDQf3CBhwcHIRVA9CnmjyruoqOKaOk0YiOMdUN9FvXrmt0R4QFePH9pmOI69DW/f39zM6btJmxCxOAvtP2+Xye2XskjwWIvcPYMT8V0cidRFGidg8GNJJNRfbaT3zubatZljUoAgO6NFGTCxUMsIqFqF/ZWOy7t6/KJug48/YXnVZ9UbZE7+mrBDJecOjkR9B/yhQAtElA1PGmBeQ8oxADfKl3TfsfSjauQOgbmXIm2vlKXZEDANLjeqoIGB86Wu+tCU4eNalyKlXFi9JIBOXVZEEPRng+XZrDXNJsNgssBqKKqPQsz1+r1ez4+DhEU4PBIDMAmTMaDAZhRQBJg+fn5zYej0PhFQAEz6F9rtGdfq7vJhUl8XdK8oBA6vyXaHw3odpi/Zf6zizdD+hZbPmrRs8sBzw5ObFOp2Nff/11JkmQ+gFUWzP7RKvrKhXK9M7nc7u8vAylrIkaSRhkEyLGDmNL26xrtRuNRlixsL+/H1YDoFNaw17p30ajEXKCGo1GcOzT6TQ4idvbWzs7O7N+v2/fffed/fjjj3Z5eWnfffddGBuwJxr167uIRYAeaH0JTIAXpbdTc8W8u8lkEmzpfD7P1IRQgObnpVPMgDrzWKa6OjG192Z39t8/Az6Ae/Abh+6LHOEv1L5ii3VKQ+1eyqbp2CeJG6baL8Gl/PtgMAg5WyRuUxdAlwhqlj/OPy/617bEWAAkBXIfUpc3miYoaqAfbF4JdC6Kwa/RltJIZtka5hyn6y9T1ZUUiHBPv+sftKiiSYR2MYBI9GNddL1eD9GWAgrO8UkrGmWROMX8J/fQ5BL2YmeQMk2gUZLvb303GrnlObsiQJA6tywgKAswnlq2YQc8TZe6nn9m3zdqlDEYeg0YNN1b4PT0NCTdaQEZxpCZhaRajMx4PLYPHz7YdDq18/PzTJQCxblYLELyEgAZnaa96tgpVkOeymq1CmvWzbIbvRDJ8fne3l4AEYBzchcYY7e3t9bv9+39+/d2dnZmFxcXdnFxYR8/fsyMV5wPTsO/p1T/63tIiUaVL0nURqZ0UZ24mYW5drVvaltjyWkauPlr89tHrzFwson496RTCYvFIoBNXSVBkir6QNtS02985tuNDulqC66l1QAvLy/DXgHD4TBMtWGvtb6LstMssfRRv39+fZdFdld14SHlQTYq0mNUOUiYMDMbjUafLW/xVDysAaJold/Q/b5YirZREatSYYrQQGfaDh0oimDJE5jP54Ha5/rNZjPMOXU6HavVaiFqh+FoNBp2cnISpkz8YONaujQHRZpMJmE7VuavYv2eelexiMgPPO+49V1qhKXnbmpkfwrCAMzrey86+DlPqUrm+cmTYXnt0dFRqCFA8SCMICwS42m1WmVKmlLCl+192fGMdeBUqFTmjR/GgL5nohl1wIBs8nzMsjuz6Vp2GAXGL9ETQBqAMp/Pg65fXFzY+fl5eC5v6PV36t3kvUN/nZfm/FOiY1WfVyNLpoag0j2DAmj0ttX/qKS+K9tvqchW/489iwZETB9RTwb7DyhQu+3b6O8HAGUqDjC6WCwC2+uZZV2FA0sLa8BSRb/WPzbVvYkNSR33GPq6FRjwg9EzADolAIVCdEDpy9ig1blDn+Hql1aQCOXbyLm8EL8WM68TFRXqcfV6PcxVrtdra7fbYX8AKk5hFAE1Nzc3ocDH/v6+/epXv7J6vR76g/ZhfMno1izVwWAQaNOzs7MwbaDvIfY8CmSKoqMUEND36dmTIkYgJT91sGD2OfjK0zkoSi0JSznh169f29dffx0SBnUbYeb92TRmtVrZxcWFzWYze//+vfX7/eDoleo8Pz8PdLtSwxpB+cqBCsCpOEeJ4tlsFqbQzCwAYxIa9/b2QpIj+QbkCczn87B66Pr62t69excKZ52dnYXCQjAavh9jLFZK1/Uc/V30Hsse+5Tigyg+M7MM7a8UfOwZinQz1Z8xcFvEpsRAV+x4z+6oHSOHQJPLDw8Pg733YIB+UkYDYGRmIXhjE6a9vb0MuABQ8TOdTm00GoW6FzBYAFiYK51iiz2b79+i47wteUy5NzOQ9z2KWalUQlal2d1+0d4BeYfP5z65BeeOkfBtUMTrKTAV/zJSczawHGYWEp60Khv1BjQZi2iPOVL6QqlhEmWYctASwsyn6vwuVG5KitDmJoYtBv6K7vuSWYOHiP5SIFhFKbyUvmk2NXP/FAnq9XphPh5KVM/T6H21uquMxqoT1R+if4r0ADIVdHsmgGeIOQuei/lUdFaLZSnTxGda+MhPFwIy2KlNd2zT0sRm2XoHPnF3G/0qcmIqL4E5KIp2Yyyg/1v/L3IwZSPYMr5h2/GnbcWeE53DJN/c3GSWlHMOdpi/zbJjjyR2vr+9vc3si6GJkthfxpUmEeb5mDL94p81rx82lU3Gxb2XFmrn+wYzR09HklEcS/zB4arzV4OEg9dzYn+rIY61zRtkHwXHoo/1+m7LyclkEujcbrdrp6enZmZhPhWDzgZKX331lVWr1bD8EVDUarWs2+2GpSrT6dQ+fvwYEr5ms5n98MMP9v3334e5XTW4Sj3RVr9eNoXgYwY0hVTLKtO2bMFzSIxiLXOO/lbxupgyCug+Tl6Lw1Av4PXr16GGgC4XpL1ae2A2m4Wo5OLiIix9IrlpMBgEZkCBMe30c+6qU8pcaaVNxuly+Wlnzf39fbu5ubE3b94E6hUAowW2qtVqACdape7m5sb6/b798Y9/tMFgYH/605+s3+/b+fl52COh3W5nggUYhTyAv2kkldLRlwACEAVTZlkH5wEjEmNvVVJOOkXr54GMoraXkRiQVjtGwmm1mt2fxieUKghQm0etG/K/OAYWGXABUwsLR74WeSzs4aF1BvS+3vZuC6RSTMNjyNa7FnrxD6+fY4joaE8/Mag1yz+VaZnXhlSbip4lNVD0OjqHZWZhvknXSTMdAq2uxZNiGbt8Ti1vvz+7Rnc+kvPPkNcfisxTg9wbhZhTz+vD1GcvARDEnP99mYJNrsWxRMi61JZkQKqfUZWPDGeurUl9RCHklajekHeiuqNJgWWfQ8debLzqVJxeS+vHIzqG9bqaF8MyQgyv7vbm26qg9qEjpvte97mkqL1f2jOlomZ0TllW9Q2qa/ou1Sb7lQOaM2F2l4yrG8PBhDHWmLL1QeqXLBuDgdhgUaNhlt21T5MJ/bFcR6MVPUZfUGqQ5qHaVFtpVwp1xRymRuWVSiUgTOomqJFm+dd6vbZ+v58xXjgElqfo3NPFxYXN53P77rvvQsY3SDgV8XBNdRi+z2i3tt9LDCjEWIQyTj52nZckZQyjGhGvlymgFLumLo8ikU63F379+rU1Go2woQw5AxoFsvMb92LqiLlM9lRn1YlOC+g8Ku3VH29Itd0wERhNIn10uN1uBxYDEHx0dBSWz5JDc3Z2FpIJVY/7/b59++23NhgM7MOHDzYajezs7MwuLy9tvV5nEndhvsidYJWRZ/f0OVO6vo0+PGWEFhNlhrQCaqrdqXFZxn7Gvs+zs/d1himWLmb3lOFCJ4j0tdib6ikMFrks7XY79KUCVP7WHQbPzs4CawszcHl5GRjvIgbqKYBCno5vYns3LkesDsI7bP+9ZnbS0fxWY+QpFsQ7YY4pG/H7v8tQXN7J+ePUQDLnpFMO6/U6TBeYWVg6petmq9VqMNxaUwA2gKkDXTGhgMO3h7b6KM07c9qukhp89432/f2Ljn8qielPHjjS3x6cpq7nxwMMkW4wRNGgo6OjzN4d5AoQ0ZPhrNQwlCWbnLBCgB0rKYGa0gd1knljSVf9eKZANxrS9d9Mf+kyLXZB1M/MPtXqoG4Gy2mhZrVCKZFaq9UKqw+ghb2z0vcT+76MPqR04aWIvg9sQoxBSTnwmN32UgYsF4GRTRxhHiCItd3sziaii5pYuV7f1a4hUbfZbIapBfQKUf8Es4Y+euYKZmDTHIHHkIfUywfZqCgVtSJ5ziNGexfd2187757arrIK7u9VqdwVQ+r1eiGSoxIcER+GkUQUlJBr3dzc2MXFha1WqxABDQaDkDPw7t27sH8786mp59bPlUKOPb9/pjLOfBuJAQDf1ueQlMPIOxbRZ9FaEjHgqqLrlylzqkWDDg4OMroBKOa9z2azTGEgnTrzWc3X19ehNCo5KbHxGTO2qfGjgMFvYLS/v2+9Xs+Ojo6s1+t9Nnc7n89DBMZc7Gq1so8fP4aIazab2Y8//mhnZ2c2HA6D7q9Wq8xOhtoW+sm/l9T/sT7IEz9mntvQe4kxVHm6XcY2xq4fOy4FePPs0qYSG1vq+Mm36Xa7tr+/b51OJwBEMws2GNDJ6jVdrePr22A/b29vA6OG04cZoM4AgJVE3DI+5TGlbFBWVjZmBmKiSNUsqyg+gtLMYz0mdW7su23augkQ8PfUNaknJyehDCxJhCR6sUwMdMmSFVDocrkMUwGahHJ5eWmj0ShUWvN7uue1PwXMipTAO+77KnWMiSjT/qeSlA6V1S/vKIr6l3r9zWYzRP3sMIje+OJQsACLxcJGo5FdXl6GtdWASepRkIFPQR6/90as7XmgUJ2sMgj+moCB09PTAIRbrZa12+3MroiAAV3GCOUKGPj48WPYY+P9+/ehwAyb0Xgw4JeNpd5R7F2VGQ96fAr0Pbd4B7SNfeS81PVjx8Wu/RA2IwYyYqwDbKzueInO6dQtAIEp3P39/RCUaaEvBQMAApw9dnk6nYYlu1dXV2HcaW2O55KUrqeOKyP3Xk3gEXiZDipz3EMg800GSewZoHe1PgK7wqkBBI0CBtbrdTBsRHpkUKNkFFcBDJD49dAJKZuAg7LOcJPvfqriozTVGYwWywXZkluNlFLrZnc15DFUWoWS7U+JSHR/AV3f7FeX8LsMMPNAQVcVYKABtzh63SmRMQArNpvNbDgcZpIcR6ORTafTkC9zeXkZpjW0CNFTGdkY+M877iWIB//6m+/5bNPgqagPHuO9eP/BbwCfrsAh76bX64WfVqsVknC73W5mrJEz4AE3wJokQFi1q6urALIp8sYUrtZ/eW4gEJOH0NGtwcBz0mj+vholeMTnpYj60s90i9iDg4Ow9So7XLGbHMUrKFVMNSoFAdBL33//vU0mE/v48WOYLz07O8tsXPHQyUp+kKUinjK04ksyjE8pqhs6V+j7C5BIPgC7DgIoSTxtNpshIjGzUBYYUDibzUJeALrEnhSAAbO79+GZpNR79qwcjl6jb4ymlkqu1+t2enoaWIHDw0M7PDy009PTYIjZlhYmABDDuvAPHz7YZDKxDx8+2NXVlV1eXtrHjx9DpjaRv+YVPIe+xe656XTmYwk6o2XbtbiOWb6NUymaakl9VzbgKyu+b9FbLV6F/T06OrKDgwM7Ojqyr7/+OiTkwrwxxnTfApy/ggHA6Ww2C1NUFxcXobYLydv9fj9UHIytXih6/sf0kUWB3qZj517MQMx5lOmEvIg9D2QofRejxvW7bV6CJkxVKpWwvAvlYkkYn5EU6A0vhYRQPsAASSmsA6fu9WMwAr5viigkj87zjt1EXhqCjklMr/KOS31HMROSlTRi1u2F0Qmz7GobnSageJCCAZyq1umPJcmVZYOQGEBASH6F4YCG1V1ItaIo5wJsYQkwqLr7pu7gRntjwPMxAcFzBjWPIQ/ZV9teK/YO88ZNihFgLMG6MqZgZNUWa86W1sUAJCsoAKCSGMieAqqfmijIeMuz0TGG40uTe08TILGXnpoTiv3211LjXDQVUYYGLfquVqtlon+UkGkC6Cf2lMcomt3VHkDxqFBF/QHqVy+XS/vhhx8CRUrRCl86dBsDlRe951HGekyeopcVnYv+kqQIEPh3gxPHKbO06fDw0I6OjjI7+mkVQXXqugoFg8POghgpaEw1RkRNfp2/b+cm4qtvEqF3Oh17+/attVqtsGESiYOwY2aWASiVSiVMc7A6ZrFYhPnX8/PzQL9SUtwD8djc+DZ65e1ISjzw4JyXwgggPugw+3zZo59KuI8t8Z+XZRp07j/WNt612d27h9aH+sfuwhA0Gg179eqVvXr1KiSw8h1Jgj5pVZfXshKHiB9G4MOHD5lib+QGaFCXNz0QA61PATBjvvE+8iBgIOasN1VCDyaKAIEeG2vHpu3X5SfMhXIfRZ4gU50j1U0pzCwAAL+xBehTWQFVMKVH7yt5TiEGFsoay5iUNbgvTYp0LnZ8bOBrNEP0QtSsNTfM7lgjyvgCBnTfcwwSRYO0lLeZZRgp7zB922LPUAYsMyZIwtIVA+QJ8IyAGdVl3RAJMMA2sKyEIEoD3JRx+PcBBGXF63LZegVPKdvY1jLnpexFUZBX9F3M5igTUKlUQr4NdpbcLJgBrR7oWQECN116qjUJ+J9VOpoXgF6ir9htBdvbss2PLQ9pc++1UdFjSB6wiCllkaJ7A+lRNPOdzWbTvvrqq5CtSmb0er0O81HsPthqtT7LSr25ubGrqyu7vf20DSurAtis6f3797ZcLu3y8jIkogAmYBZ88aVt+k5/+88fU740IKBSFkTGKp15gTUgOtHqkfq3Xm+1WoVkOxJMdYMeHLNvr6fz+Y52xJ5Tf+vnfhwdHh6GLYffvHkT1mjX63U7Pj62V69eZcBApVIJY4As7PPzcxuPxyEvgKWSTA2s1+vM9Im+h6ekXNW+eGbgpcl92xTrz02BRVkwaZYtmcxvHD9gWXe4xPEDAtA57G6v18tUD6QsMfu8UKAL+wzNT92Wjx8/htVbbP52dXUV8nQ0ITfGTsWe9Sn1xAchDykPNk2wrcSMl34WAwRe/Jxn6h4xgV6iTny73Q5gAGTJdxSMabVaoQIc9yTiu7m5CcknFI4ZjUahQhyJKBRVKVNLYBNRY7YJtfqlOvJNJaU/ZY1c0WeI5gUwfz6bzez29jZEMWq0mB4ASKpRJeLR+yrdynHekcWYsyLAyO92u22np6chERIDXK1WbX9/3w4PD0MtBej0Wq0WnP5gMMiAAc9wEBH6DYceM+jIA6zeyH6J4LYsA7AJs7oJOIr1mX+fCgZw+NQCgA1galYZgV6vF6ZqdTxUKpVwDDk2gHEtI3x1dWXj8TjUtoCxYgqXcagbY/nplrx+ekwpy+562VSHH2xpYSyif+hr+3uUOT+l+Bjk/f19e/XqVQZtoky666BWuGLXwk6nE6q+EdWxWQx0E+tStYgQz4HiPkQ/le2Tn6s8hE6mjANg0MzCO9fqm4BGpgJYCUA9CjZFYU2+mUUTU/X+qbbod2WeV69XqVRCNjZFtUjWIkojqUspXt1+m1Uyl5eXoXgWTJjWMihbBTH2fD93uQ9r4gOtTa5ZZOM9u6PXA/hB6xNY6YoBbC3AEtYWZrbZbAbnXq1WbblcWq1WCxsMMRWrBbyYovrxxx8z5a6ZFtAVAjE2INY/m/i5sjpedH7eZ08+TZBqVB6Nr8ekrueVMHV+HkOwidFDyI4+Pj62r7/+OiyLMrsDA91u146PjzPrXTH8zF8xDUAFwfl8HuagWKLiaV7mtR6aCeC3dyIPpSzbKPZLMOQPBVh1zb1Z1pFiUGazmVUqlUyWvP7mGMAhCU6wB2o8fQ6Jr5zGc8RAb4zi9FQ4gk6TRHt0dGSvX7+209PTMG8LQGApLcYdNoDaAT/88IP9+OOPITcG+tUXDMorix3T25egRy9BPCNkVn5cFkXtZcdDnk2P2XXsHwmpJPodHh5mlg3C0CJ+WW69XreLiwu7uroyMws7bgK2h8OhDYfD0EcsY51Op/bnP//ZxuOxXV1d2Wg0ConbCgLylgyn+iyv3/KC0SLJY7Aeayw86TRBGbBQxsnnUVcpukS/r1QqmQJCiEevSo3qchWlfKFEWZJC8glzVWoEt41Oi15+EZqP9ce2CvUS51HLyH2ZAQ+ufISFXrBKQOtFYJyYi2QfdJKaYuwQ14uBuphD8Of6tvvzaTPHwpTpBlxQtbr/hjIWRF8kYVEm2S+B5N5+SkDbkhftPBbzdR9j/dxy32mM2HPfZ4ykgkW1qUwFwDbBNPGbxEBEcwdILuTHzMLUU7/ft1qtFqYCGFMUdyNpEJbWLxUsy6L91OVeRYe8pFBVGXopdq08xiEmfqdErqPn4tx7vV5IglosFpmiMFS2ImOV9eNmFhDlxcWF9ft9u7q6sm+//dbm83nYWx5Dr85AcwNoK0xD3lRBWSAQYwQeWn4qA2Zbo0ckwjViG3GxpE6NFvfyO6T5NmnddPSHzzGser3Y2EhRt94BaztqtVpIoj05ObGTk5MwRaCJXt5BkwMzGAzsj3/8ow0GA/vhhx9sOBwGOpdjqVegIETbGlsdoQ7vIViCvCDiS5IyQCAVZMUi97z/i65rli76RtDFkluKt7E6RZcG6jQB+tpqtcLyXK5NZU+qua7Xa/v48aOt12sbDoehpgWVOj98+JBZ2g1QjTFnqecrkrw+ewl6Vna8bAUGthmMHhDkSQoIlGEWYtfyx/tdBxEQqEZD3njj6EGbijiZGy1bpUrb5Nu6SbSfQuVlPnsqee57x+j9Ta/h/449EwlIvgZAKgLxztpH/9pef0wRWC4C7Hpd3eedcaFbwPo5fpIfqRVAMS0Msd9bQNuU6gf9PtX+++qR14Uyxh/A9FLkIRxMDCyUsT9FIMFfwxcQ8gWCWB6o9ljP457YVGws23ejh7C0Wv6degEAU10poM/ideI+farXfYhr3lc2uf+DTBNsopxlBp83cpsgLx8B+e8wbroevNPphAhICw2t13d7W2PcyQf44YcfQrEKisLovK/fkEnboxTwfcUzA/6zWL88lTz3QFC5bzSYYr1im3TFnHgK3Op10BvO9UVlOB/aNa90dd67jz0DhbSoJ0AODbstYqTNLCTEskrm6urKvv/++1AuWedh9X46N+unzxDNy8gDXY8hef30EiI8s8/btEnfxECkv1bZ5ywKVmBTlW2lTDerAgAHWqZbi2nd3t6GpGwKVpGbQr0K3UgLoMCSXqp3YptTeVplnj2mG2UA7UPqTSpoTIH+TcfNg+xayM1T5+Q57NS1iqKeojbmIVhVVkWmTCFonXaiHBQNKuri4sIuLi5CdKTGW41cGUq3DOVX9vM8QFB0Pf/dQ0Ugzy3bMAN5gz82GDUyKoqwUp9p9B2bUlAnqceVeYa8sahjwZdzrVarYTkXxpSpMGUExuOxjUajTH/5v/V5yuj+U+pOarw89D4hjyEPyRRsw57l6bMuFdTy1co8YYP5XIMvlgdSG4CNrdijQ8EAtlrre/gKgpsyHpvKfYMO5DlYhWdPIHwI8Y42ZYxja5orlUpmTnS9XoesVCh/SsheX1/b+fm5TSaTkJiC0qoT0Kjf02exaFEjPn98GYVIGbJNlSl2vqfSfq7iHXCM2SkCbb4P/TW9A+X/WEEh1ZttDTd/s8kWO3OaWTDSLOFicxwzC9XbYAQwzn6zHC0oFOsHBTb+3G0M4X0MaIyJfKmSet950WjMRmw7pv15HjDpHh0kC+pOl5QYBnhSvXKxWFilUglJqYvFIiwN/PjxY9gvgORU7K9fDaA22dvmbd9v2X6KMQaPJSn7s+0zbswMbKs8nFsGmW0TnaYiN67nIyqOg3JVutfPPenOayglyprXFg88Ym3S77TOQZ4UMQIxA1wkseMf0jC+dANbJKkoO9VHHvQVXddHzHpuTG/5fV9WCafd7XZDdTed59X13CR3jcdjOz8/t7OzM/v+++/D6hnfdp90mBIPfjdlsx5KvkSwm7J5Rc9SxATk9XdR8EWyKCsEWDmwv78fpqLQr0qlEpgAplnZl4Pl2uxrMZ/Pg64QoOU5300/v688Fwjwn3mwXVa2KkecauAmjptzyog+XBmaPXWc/s2yFI1goJaoBqcKSmKU7jJY9llj/RIbSDFAVKbfYw5jU0lFmZu+qy9JYs9bVo/zAKsHVimgm3fN2D1SoCPFNvjvNB+BpDit7a71NHDmZhboV+ZfLy8vQ1Eh9hjw7fUBQKpPN2G+7isx8JY35nBsL11SNgQpGxg8FPtHv5GfRZEhTcrWDdp4L7pLJ9sL+1UAHB9bFrhNEPmSJaarj2mH7703waaMwaYvKUWxqgJoRJ1yZr4j2TiFOU+yUZkL1ZUBnoKKGeDYsxWhVv9dUU2C2D23YQJi7y/W/tS9y1x/W4biKSQvGkoNwJTDT0VHsX6N3dcj+DIG2bMJKjhzn1zIOKnVaiEpluStTqeTqauBEWdcMRbOz89tNpvZ999/bx8/fgzLCtfrdTje63VqDKR00Ot1mX5IXUtF26FFjzwjwXGxmggvRWJO4T5gi+MewoHSNmUGmC7odrvhPhRrI8DSktzYZE0AxA7nPXMR8PwSpehdFzF/m8i9cgaKOr5sRPSQUkR/YSRhBur1elA03TeeZShEPR6FPnSbi54ndlzR//e5709NUuySfsffZsWMSIom9eek3lGeHpVlHVKgsawAENTxeSfIWLm+vg77v7OUNmagY8+8SXs2vU4ZIPBTlJfg8DbRPa34t16vM+WAsbsUrNIS1iwbfCzb+yXIfRmBsuduDAa2bdR9XmTZc9Xg+0gLWonqbxg3PcaXp1RaMRXtPcTz0YaXLD8FxO0BQVkqW8996Pbc5x4471g07q/vx4GZhR3jWq1WAAGwBWykNJ/PwyZbP/zwg00mk7C8S5MK9b6bPvdjyH0ZqcemZLeV2FSH/97/n9KrFKuVd2+O8efEwDSrAczMzs/PbW9vL5MEyJJs7C6OX4GDTwAski/dRqUkZbfM4ozmNrLV0sKnGCRlqdJYWzwoiNGWukkQ3xetQ9Vrb9LWbeQ+0Z6/Tlm69THlOQepN1bbMAT6Wd7n/nqpY4rOKWImUtfJa1fsebXUNkyBVgqEKZtOp4EZgDlLGaZNnjP13X101tuGnwKQjUlZB+D1fZP+KBoT/li9J44dPYP2BxCMRqPAPGnw5UHAT/HdPZVsYtuffAvjssqrf5eZd9RjY8fFisNoXYA8I5rHCNxXYv3x0FGJDuaYobyPeNo6dt+fquQZ1bL9et/3QL+jy3njxLNd1IrXH5aCUdSFnd5I5qKi283NTWbu3ddC2OY57iNF/Rhzambp9vrPXlK9AW/HyoyzGHAre69NBUZgvV7bxcVFmIoys8DMan2WWPn41BTcz1liQUwZpujRpgkeQso617wIfBPno8fGBlEsgot9VkZBU8697LFlpMx5ZRxVWUdUFKU+NWv0mJKKdFMsQophiP2dut5D6IG2Iwb++JzvdAMZ3ZCI7G/Kt7L/ABGdru0uE8EXPeM27EnR+WXvpf21yXWeU1TnNgVeTzX1AaOkegiYIv9Eg7HHDrB+SoAiZWvybHTZ539xzIA3aEVgocgg+vuS8ZwCCLF7eCT2VFLmXimDHHMMakge2ihs4hieQ/Rdx5igl2AwtqFyUxFiCszG/jazsIqA+u/j8ThsxjUcDsMKhBgVnALWX4p8aWB20/GbFzXq76LjU9f27cHxxwppxUpob8NYbCIvZXw/luTpw6NMEzzkICl6OXmAIEaTeCSfOk+TnXTXQOanYnTqS1WiMlFUrJ9i/VVkLPKiY38+oOAlGdWYk41RxTFnWhaQbcKelDmu7ADfREdT79/MQu4AOQKDwcDevXtn4/HY+v1+JkeAH43uYm2Ltb9stP8Q+rNtW16a/qZE3+MmYKxIX+4bLKjDp5ZAUf8XsZib6PhDXOdLEW/XU+O7jGzNDBRR5Y/Z6d5g5tFC3slr+zSyid2jLL1f9KzbUHllnVDReXkD259z3wHzJRjQPCl655tOD5V1fGWP9efFKhaW0UXeOwleFBVimVetVrPRaBSmBfjeLzvc1GnmHbttP5Q5vwwQ+9J1F9nEkafs5GNIHvvwkL6i6DnygNNDtONLZiHuPU2wCShIDcJNI5u877mHJjTFoqdK5S7pimNjEW7sc73uQ0hMKR9iYCpDEouI/b3KOhJtc1mW4UuVFKtQ1G95UWkZBL+pUdFEvqL20GaqClYqFZvNZrZer0OuwLt37+zdu3ehBryZhbLdjA1NINTrxuQxgECe7qUAizIbsev5778Efd4kAMg7f1sgnHd8yumXtZ/b2Ng8tljbGGv3tvdMjfenBgZe7zfRg0fbmyDlfMocXyYC28RxxaRslJdnaMq0o6iNZdtXdGzqeVPOK+XYUvfPa8eXYDDLRv0x/crrt9i1yzj5bfWqyKkWRe3aVmUGlstlAAUkDl5fX4eiW6l7bdLmMoCg6LjUNfPGbAyMlQ1UXqKk+nlbx/kQx3xJUvRetwE/Rd89Zh/GbPs293yQBMKHjJBTBjd2bBljlOfk1ut1Zp11DGEXXSeFgh9atqX/9FxF47GdFR+6HS9N8vQr5hyKqM28d5/n3Mo4vphex5wcf+v2x7RN27derzN6vl6vM9XfWFFAKW5Kc5tZ2MnQLMuisTsdLIPv2yKnHHvevL4qc45nKmKgSD/zbdUdIl+ynvMOveEv0ks9v+j6sfNjY6RMIPGYzvC+QG5bu1rmWk8FpNbrdZTh3kQebDVBniPeJoovAgKp66vDi72Y2DlqAMpQ6/7em3b+YxmZIiYk9jxF0VHZe5Zxas8lMaefAgRImfdadEzZaDf2Xep9pRxsDCAUjT8KvpiZTadTq9frNplMbLFYhI26KpVKZoMj7SsAAWBA2xhrR9l+uC8gyOsvbV/etfz3X8KGRUieXpZhFcqMgzIBW+p+mwQfRVLWvqTumWe3Nm3jffrpPlI2gC6Se4GBMrTrplJkwPyAVeetxiomsfnNomukjLFvi7a5rNLdp59SUewm7yQPGKTa7M/bxtE+taArKeT8WAO1jBMs6pdUH8YiWYS+x1H7aF51nBKwg8HAqtVqqAnvN80yu8tHUH3XfeO5RwoMbAucNgED3pH7tqSuETs/lhPx0kTfZcxupYBu7Lf/nvPKBHOptm3yeZ4tKnM//b6sHym6TpFd5bttQNFDSpl2FsmDJhDqC9jUEfiXl3e+RiV+L4G868cMA8ZSgUBqcKiTi13LG8vUM6b+13ttImXOiSlsTIHLRAX+evyOffacIADxbfFtuk87i4zApo6w7Pex6/qoPaVvCgooFwtDoHS/Tj2k7u/Lxmpp45SDLnrOvOfNOyd2fqwvUu3xQEDtzEvQYy9FeuuBm/7e5B5eyoLnTY7jt9qSvGO3kcd8h3mg60uRrbYwLnK895EUoIhJUcGTWJuLjHPZqCRFq6Xa/BTGpOi+RYCgDFjw33lHmzJQzz0wYnrlf/vjy0REeefH/k61q+g9pc5JGfkihxdzkGb5Rbm8c9TPmUpI3a9IHgMIFJ2/aRtfkqSie28LYzpUpNcp26ZSBgSXHfNe3/P0fxPG4CGi5U3Pf247dx/ZmBnY5GHzFHKTa6TYBw9KvEHQqN9TxEWRkz+G+2j1rJQDSbU5715PpbDeWec58m2ZgZcm3nH532Ucet77jD130f95wLSM8/LPFms7zJk/zycb6vdE9nyn7EG9Xg/UeaVSCbUJKpWK7e3thc+2AcN5DrkMGIj9zvvM940eo6xADDi9FNnWaXrJc9ypQCv2mQeNaqOLxlYMzGwCKPRc/ftLds5PLU8yIbbNYCoTTeV9l+fEtrn/Nu2PRV9FUcxOnkbKRM3bvjsPJouc0yb3yPtuU0p202N824quUeYZ884tAxC2sRM/pXH3VM7uS3KqX1JbX5JU1rue28lOdrKTnezkZy0vO1V2JzvZyU52spOdPLrswMBOdrKTnexkJz9z2YGBnexkJzvZyU5+5rIDAzvZyU52spOd/MxlBwZ2spOd7GQnO/mZyw4M7GQnO9nJTnbyM5cdGNjJTnayk53s5GcuOzCwk53sZCc72cnPXHZgYCc72clOdrKTn7n8f13hUfopxFprAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot 3 examples from the training set\n",
    "check_data = first(train_loader)\n",
    "fig, ax = plt.subplots(nrows=1, ncols=3)\n",
    "for image_n in range(3):\n",
    "    ax[image_n].imshow(check_data[\"image\"][image_n, 0, :, :], cmap=\"gray\")\n",
    "    ax[image_n].axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe1292d",
   "metadata": {},
   "source": [
    "## Prepare validation set data loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e40efd25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-08-06 18:30:29,443 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
      "2024-08-06 18:30:29,443 - INFO - File exists: /tmp/tmpy_blw935/MedNIST.tar.gz, skipped downloading.\n",
      "2024-08-06 18:30:29,444 - INFO - Non-empty folder exists in /tmp/tmpy_blw935/MedNIST, skipped extracting.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading dataset: 100%|██████████| 5895/5895 [00:01<00:00, 3671.89it/s]\n"
     ]
    }
   ],
   "source": [
    "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)\n",
    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"Hand\"]\n",
    "val_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.LoadImaged(keys=[\"image\"]),\n",
    "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
    "        transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True),\n",
    "    ]\n",
    ")\n",
    "val_ds = Dataset(data=val_datalist, transform=val_transforms)\n",
    "val_loader = DataLoader(val_ds, batch_size=64, shuffle=True, num_workers=4, persistent_workers=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26cfc67",
   "metadata": {},
   "source": [
    "## Autoencoder KL\n",
    "\n",
    "### Define Autoencoder KL network, losses and optimiser\n",
    "\n",
    "In this section, we will define an autoencoder with KL-regularization for the LDM. The autoencoder's primary purpose is to transform input images into a latent representation that the diffusion model will subsequently learn. By doing so, we can decrease the computational resources required to train the diffusion component, making this approach suitable for learning high-resolution medical images. We will also specify the perceptual and adversarial losses, including the involved networks, and the optimizers to use during the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f9044d57",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\")\n",
    "\n",
    "autoencoderkl = AutoencoderKL(\n",
    "    spatial_dims=2,\n",
    "    in_channels=1,\n",
    "    out_channels=1,\n",
    "    channels=(128, 128, 256),\n",
    "    latent_channels=3,\n",
    "    num_res_blocks=2,\n",
    "    attention_levels=(False, False, False),\n",
    "    with_encoder_nonlocal_attn=False,\n",
    "    with_decoder_nonlocal_attn=False,\n",
    ")\n",
    "autoencoderkl = autoencoderkl.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "177b96fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "Downloading: \"https://download.pytorch.org/models/alexnet-owt-7be5be79.pth\" to /home/localek10/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth\n",
      "100%|██████████| 233M/233M [00:02<00:00, 113MB/s]  \n",
      "You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"
     ]
    }
   ],
   "source": [
    "perceptual_loss = PerceptualLoss(spatial_dims=2, network_type=\"alex\")\n",
    "perceptual_loss.to(device)\n",
    "perceptual_weight = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4aef349e",
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator = PatchDiscriminator(spatial_dims=2, num_layers_d=3, channels=64, in_channels=1, out_channels=1)\n",
    "discriminator = discriminator.to(device)\n",
    "\n",
    "adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")\n",
    "adv_weight = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae58c6a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_g = torch.optim.Adam(autoencoderkl.parameters(), lr=1e-4)\n",
    "optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=5e-4)\n",
    "\n",
    "# For mixed precision training\n",
    "scaler_g = GradScaler(\"cuda\")\n",
    "scaler_d = GradScaler(\"cuda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb27c18",
   "metadata": {},
   "source": [
    "### Train model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba88de6d",
   "metadata": {},
   "source": [
    "It takes about ~55 min to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4794275a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100%|██████████████████| 125/125 [00:48<00:00,  2.59it/s, recons_loss=0.107, gen_loss=0, disc_loss=0]\n",
      "Epoch 1: 100%|█████████████████| 125/125 [00:51<00:00,  2.44it/s, recons_loss=0.0523, gen_loss=0, disc_loss=0]\n",
      "Epoch 2: 100%|█████████████████| 125/125 [00:51<00:00,  2.44it/s, recons_loss=0.0428, gen_loss=0, disc_loss=0]\n",
      "...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 90: 100%|████████| 125/125 [00:58<00:00,  2.14it/s, recons_loss=0.0166, gen_loss=0.461, disc_loss=0.185]\n",
      "Epoch 91: 100%|███████████| 125/125 [00:58<00:00,  2.14it/s, recons_loss=0.017, gen_loss=0.453, disc_loss=0.2]\n",
      "Epoch 92: 100%|████████| 125/125 [00:58<00:00,  2.15it/s, recons_loss=0.0166, gen_loss=0.468, disc_loss=0.188]\n",
      "Epoch 93: 100%|█████████| 125/125 [00:58<00:00,  2.14it/s, recons_loss=0.0165, gen_loss=0.44, disc_loss=0.201]\n",
      "Epoch 94: 100%|████████| 125/125 [00:58<00:00,  2.14it/s, recons_loss=0.0165, gen_loss=0.449, disc_loss=0.198]\n",
      "Epoch 95: 100%|█████████| 125/125 [00:58<00:00,  2.14it/s, recons_loss=0.0166, gen_loss=0.44, disc_loss=0.213]\n",
      "Epoch 96: 100%|█████████| 125/125 [00:58<00:00,  2.15it/s, recons_loss=0.016, gen_loss=0.456, disc_loss=0.184]\n",
      "Epoch 97: 100%|████████| 125/125 [00:58<00:00,  2.15it/s, recons_loss=0.0166, gen_loss=0.462, disc_loss=0.198]\n",
      "Epoch 98: 100%|████████| 125/125 [00:58<00:00,  2.14it/s, recons_loss=0.0168, gen_loss=0.438, disc_loss=0.204]\n",
      "Epoch 99: 100%|████████| 125/125 [00:57<00:00,  2.16it/s, recons_loss=0.0167, gen_loss=0.461, disc_loss=0.194]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 100 val loss: 0.0186\n"
     ]
    }
   ],
   "source": [
    "kl_weight = 1e-6\n",
    "max_epochs = 100\n",
    "val_interval = 10\n",
    "autoencoder_warm_up_n_epochs = 10\n",
    "\n",
    "epoch_recon_losses = []\n",
    "epoch_gen_losses = []\n",
    "epoch_disc_losses = []\n",
    "val_recon_losses = []\n",
    "intermediary_images = []\n",
    "num_example_images = 4\n",
    "\n",
    "for epoch in range(max_epochs):\n",
    "    autoencoderkl.train()\n",
    "    discriminator.train()\n",
    "    epoch_loss = 0\n",
    "    gen_epoch_loss = 0\n",
    "    disc_epoch_loss = 0\n",
    "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=110)\n",
    "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
    "    for step, batch in progress_bar:\n",
    "        images = batch[\"image\"].to(device)\n",
    "        optimizer_g.zero_grad(set_to_none=True)\n",
    "\n",
    "        with autocast(\"cuda\", enabled=True):\n",
    "            reconstruction, z_mu, z_sigma = autoencoderkl(images)\n",
    "\n",
    "            recons_loss = F.l1_loss(reconstruction.float(), images.float())\n",
    "            p_loss = perceptual_loss(reconstruction.float(), images.float())\n",
    "            kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3])\n",
    "            kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]\n",
    "            loss_g = recons_loss + (kl_weight * kl_loss) + (perceptual_weight * p_loss)\n",
    "\n",
    "            if epoch > autoencoder_warm_up_n_epochs:\n",
    "                logits_fake = discriminator(reconstruction.contiguous().float())[-1]\n",
    "                generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n",
    "                loss_g += adv_weight * generator_loss\n",
    "\n",
    "        scaler_g.scale(loss_g).backward()\n",
    "        scaler_g.step(optimizer_g)\n",
    "        scaler_g.update()\n",
    "\n",
    "        if epoch > autoencoder_warm_up_n_epochs:\n",
    "            with autocast(\"cuda\", enabled=True):\n",
    "                optimizer_d.zero_grad(set_to_none=True)\n",
    "\n",
    "                logits_fake = discriminator(reconstruction.contiguous().detach())[-1]\n",
    "                loss_d_fake = adv_loss(logits_fake, target_is_real=False, for_discriminator=True)\n",
    "                logits_real = discriminator(images.contiguous().detach())[-1]\n",
    "                loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n",
    "                discriminator_loss = (loss_d_fake + loss_d_real) * 0.5\n",
    "\n",
    "                loss_d = adv_weight * discriminator_loss\n",
    "\n",
    "            scaler_d.scale(loss_d).backward()\n",
    "            scaler_d.step(optimizer_d)\n",
    "            scaler_d.update()\n",
    "\n",
    "        epoch_loss += recons_loss.item()\n",
    "        if epoch > autoencoder_warm_up_n_epochs:\n",
    "            gen_epoch_loss += generator_loss.item()\n",
    "            disc_epoch_loss += discriminator_loss.item()\n",
    "\n",
    "        progress_bar.set_postfix(\n",
    "            {\n",
    "                \"recons_loss\": epoch_loss / (step + 1),\n",
    "                \"gen_loss\": gen_epoch_loss / (step + 1),\n",
    "                \"disc_loss\": disc_epoch_loss / (step + 1),\n",
    "            }\n",
    "        )\n",
    "    epoch_recon_losses.append(epoch_loss / (step + 1))\n",
    "    epoch_gen_losses.append(gen_epoch_loss / (step + 1))\n",
    "    epoch_disc_losses.append(disc_epoch_loss / (step + 1))\n",
    "\n",
    "    if (epoch + 1) % val_interval == 0:\n",
    "        autoencoderkl.eval()\n",
    "        val_loss = 0\n",
    "        with torch.no_grad():\n",
    "            for _val_step, batch in enumerate(val_loader, start=1):\n",
    "                images = batch[\"image\"].to(device)\n",
    "\n",
    "                with autocast(\"cuda\", enabled=True):\n",
    "                    reconstruction, z_mu, z_sigma = autoencoderkl(images)\n",
    "                    # Get the first reconstruction from the first validation batch for visualisation purposes\n",
    "                    if _val_step == 1:\n",
    "                        intermediary_images.append(reconstruction[:num_example_images, 0])\n",
    "\n",
    "                    recons_loss = F.l1_loss(images.float(), reconstruction.float())\n",
    "\n",
    "                val_loss += recons_loss.item()\n",
    "\n",
    "        val_loss /= _val_step\n",
    "        val_recon_losses.append(val_loss)\n",
    "        print(f\"epoch {epoch + 1} val loss: {val_loss:.4f}\")\n",
    "progress_bar.close()\n",
    "\n",
    "del discriminator\n",
    "del perceptual_loss\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a89d919",
   "metadata": {},
   "source": [
    "### Visualise the results from the autoencoderKL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f53bccdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot last 5 evaluations\n",
    "val_samples = np.linspace(max_epochs, val_interval, int(max_epochs / val_interval))\n",
    "nrows = min(5, len(intermediary_images))\n",
    "fig, ax = plt.subplots(nrows=nrows, ncols=1, sharey=True)\n",
    "ax = ensure_tuple(ax)\n",
    "for image_n in range(nrows):\n",
    "    reconstructions = torch.reshape(intermediary_images[image_n], (image_size * num_example_images, image_size)).T\n",
    "    ax[image_n].imshow(reconstructions.cpu(), cmap=\"gray\")\n",
    "    ax[image_n].set_xticks([])\n",
    "    ax[image_n].set_yticks([])\n",
    "    ax[image_n].set_ylabel(f\"Epoch {val_samples[image_n]:.0f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f6f3cd",
   "metadata": {},
   "source": [
    "## Diffusion Model\n",
    "\n",
    "### Define diffusion model and scheduler\n",
    "\n",
    "In this section, we will define the diffusion model that will learn data distribution of the latent representation of the autoencoder. Together with the diffusion model, we define a beta scheduler responsible for defining the amount of noise tahat is added across the diffusion's model Markov chain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "58188b0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "unet = DiffusionModelUNet(\n",
    "    spatial_dims=2,\n",
    "    in_channels=3,\n",
    "    out_channels=3,\n",
    "    num_res_blocks=2,\n",
    "    channels=(128, 256, 512),\n",
    "    attention_levels=(False, True, True),\n",
    "    num_head_channels=(0, 256, 512),\n",
    ")\n",
    "\n",
    "scheduler = DDPMScheduler(num_train_timesteps=1000, schedule=\"linear_beta\", beta_start=0.0015, beta_end=0.0195)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffa7397b",
   "metadata": {},
   "source": [
    "### Scaling factor\n",
    "\n",
    "As mentioned in Rombach et al. [1] Section 4.3.2 and D.1, the signal-to-noise ratio (induced by the scale of the latent space) can affect the results obtained with the LDM, if the standard deviation of the latent space distribution drifts too much from that of a Gaussian. For this reason, it is best practice to use a scaling factor to adapt this standard deviation.\n",
    "\n",
    "_Note: In case where the latent space is close to a Gaussian distribution, the scaling factor will be close to one, and the results will not differ from those obtained when it is not used._\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f9bd963f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaling factor set to 0.9529204368591309\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    with autocast(\"cuda\", enabled=True):\n",
    "        z = autoencoderkl.encode_stage_2_inputs(check_data[\"image\"].to(device))\n",
    "\n",
    "print(f\"Scaling factor set to {1/torch.std(z)}\")\n",
    "scale_factor = 1 / torch.std(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d71b5ca",
   "metadata": {},
   "source": [
    "We define the inferer using the scale factor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "25dfae5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "inferer = LatentDiffusionInferer(scheduler, scale_factor=scale_factor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286f147e",
   "metadata": {},
   "source": [
    "### Train diffusion model\n",
    "\n",
    "It takes about ~80 min to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3e1f1a0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100%|██████████| 125/125 [00:33<00:00,  3.76it/s, loss=0.393]\n",
      "Epoch 1: 100%|██████████| 125/125 [00:34<00:00,  3.63it/s, loss=0.162]\n",
      "...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 199 val loss: 0.1034\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:11<00:00, 89.47it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimizer = torch.optim.Adam(unet.parameters(), lr=1e-4)\n",
    "\n",
    "unet = unet.to(device)\n",
    "max_epochs = 200\n",
    "val_interval = 40\n",
    "epoch_losses = []\n",
    "val_losses = []\n",
    "scaler = GradScaler(\"cuda\")\n",
    "\n",
    "for epoch in range(max_epochs):\n",
    "    unet.train()\n",
    "    autoencoderkl.eval()\n",
    "    epoch_loss = 0\n",
    "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
    "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
    "    for step, batch in progress_bar:\n",
    "        images = batch[\"image\"].to(device)\n",
    "        optimizer.zero_grad(set_to_none=True)\n",
    "        with autocast(\"cuda\", enabled=True):\n",
    "            z_mu, z_sigma = autoencoderkl.encode(images)\n",
    "            z = autoencoderkl.sampling(z_mu, z_sigma)\n",
    "            noise = torch.randn_like(z).to(device)\n",
    "            timesteps = torch.randint(0, inferer.scheduler.num_train_timesteps, (z.shape[0],), device=z.device).long()\n",
    "            noise_pred = inferer(\n",
    "                inputs=images, diffusion_model=unet, noise=noise, timesteps=timesteps, autoencoder_model=autoencoderkl\n",
    "            )\n",
    "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
    "\n",
    "        scaler.scale(loss).backward()\n",
    "        scaler.step(optimizer)\n",
    "        scaler.update()\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "\n",
    "        progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
    "    epoch_losses.append(epoch_loss / (step + 1))\n",
    "\n",
    "    if (epoch + 1) % val_interval == 0:\n",
    "        unet.eval()\n",
    "        val_loss = 0\n",
    "        with torch.no_grad():\n",
    "            for _val_step, batch in enumerate(val_loader, start=1):\n",
    "                images = batch[\"image\"].to(device)\n",
    "\n",
    "                with autocast(\"cuda\", enabled=True):\n",
    "                    z_mu, z_sigma = autoencoderkl.encode(images)\n",
    "                    z = autoencoderkl.sampling(z_mu, z_sigma)\n",
    "\n",
    "                    noise = torch.randn_like(z).to(device)\n",
    "                    timesteps = torch.randint(\n",
    "                        0, inferer.scheduler.num_train_timesteps, (z.shape[0],), device=z.device\n",
    "                    ).long()\n",
    "                    noise_pred = inferer(\n",
    "                        inputs=images,\n",
    "                        diffusion_model=unet,\n",
    "                        noise=noise,\n",
    "                        timesteps=timesteps,\n",
    "                        autoencoder_model=autoencoderkl,\n",
    "                    )\n",
    "\n",
    "                    loss = F.mse_loss(noise_pred.float(), noise.float())\n",
    "\n",
    "                val_loss += loss.item()\n",
    "        val_loss /= _val_step\n",
    "        val_losses.append(val_loss)\n",
    "        print(f\"Epoch {epoch} val loss: {val_loss:.4f}\")\n",
    "\n",
    "        # Sampling image during training\n",
    "        z = torch.randn((1, 3, 16, 16))\n",
    "        z = z.to(device)\n",
    "        scheduler.set_timesteps(num_inference_steps=1000)\n",
    "        with autocast(\"cuda\", enabled=True):\n",
    "            decoded = inferer.sample(\n",
    "                input_noise=z, diffusion_model=unet, scheduler=scheduler, autoencoder_model=autoencoderkl\n",
    "            )\n",
    "\n",
    "        plt.figure(figsize=(2, 2))\n",
    "        plt.style.use(\"default\")\n",
    "        plt.imshow(decoded[0, 0].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
    "        plt.tight_layout()\n",
    "        plt.axis(\"off\")\n",
    "        plt.show()\n",
    "progress_bar.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ac6265",
   "metadata": {},
   "source": [
    "### Plot learning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a29c1b9c",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7cb88b069580>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title(\"Learning Curves\", fontsize=20)\n",
    "plt.plot(range(1, max_epochs + 1), epoch_losses, linewidth=2.0, label=\"Train\")\n",
    "plt.plot(range(val_interval, max_epochs + 1, val_interval), val_losses, linewidth=2.0, label=\"Validation\")\n",
    "plt.yticks(fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.xlabel(\"Epochs\", fontsize=16)\n",
    "plt.ylabel(\"Loss\", fontsize=16)\n",
    "plt.legend(prop={\"size\": 14})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb02dd3",
   "metadata": {},
   "source": [
    "### Plotting sampling example\n",
    "\n",
    "Finally, we generate an image with our LDM. For that, we will initialize a latent representation with just noise. Then, we will use the `unet` to perform 1000 denoising steps. For every 100 steps, we store the noisy intermediary samples. In the last step, we decode all latent representations and plot how the image looks like across the sampling process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d3b478d2",
   "metadata": {
    "lines_to_end_of_cell_marker": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:08<00:00, 121.57it/s]\n"
     ]
    }
   ],
   "source": [
    "unet.eval()\n",
    "scheduler.set_timesteps(num_inference_steps=1000)\n",
    "noise = torch.randn((1, 3, 16, 16))\n",
    "noise = noise.to(device)\n",
    "\n",
    "with torch.no_grad():\n",
    "    image, intermediates = inferer.sample(\n",
    "        input_noise=noise,\n",
    "        diffusion_model=unet,\n",
    "        scheduler=scheduler,\n",
    "        save_intermediates=True,\n",
    "        intermediate_steps=100,\n",
    "        autoencoder_model=autoencoderkl,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1eeea976",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 639.5, 63.5, -0.5)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NDQRMq9WyEAyUvFf4ALbr16+rWCyakpmcnFQ2m+24X7PZ1P379y20N5fLGcBm02KwSjLWkfcJ0Q6VfLghvBHvFS2Cx3tOUaAIHB8qtbq6qkKhoEwmY+FqALVUKqVqtapKpWKKhEn3YXTJZFKpVEp9fX2mWFAqKAI/8bCGCDT6hRBNJpNqtVq2cfv7+02BEG5OH/EWcx+UOyFR7Xbb5hZQCOiv1WpdmRy/OKXnQtl7SvznMNaYI4xIPKuMN8QAzwPAZZ5OT0+VTqc1PDxsYAtAgEDzhgGKxANj2DrG0xshKCNAWhjyHolENDg4qHw+r56eHo2NjWl3d1fz8/O2mQkh9mODwAQE1ut1/dVf/ZXm5uY0MjKiXC5nimtkZERjY2M6OjoyJeeNr/MEVSKRUDabVaFQMA+ENw4w3vkshuL29rYGBwc1MDCggYEBLSwsGLlTLBaVTCbNa0BI58rKinZ2drS9va2tra0OQsbLDq/IvKDz+6Sbkmb9QEz4MeC1VqulfD5vcxzKgNBwYT94Br5er6vdPouiyOfzOj09Vb1e19ramoGgZDJp9/Qkh5ed3iDlNdabB2xexpJ2QUsmk8pkMkomk/ackEDIO6JCTk9PbX1yrW6KlGcDKLK28Uh5o4BrQAr29/fbGOzv79va8wCAPew9e37sz1PUrIEw1N83b3y9CBz4se6mdMPx99dGbjCehUJB165dUzQa1cHBgZaWlizlwT9vN6Xerb+sb8YWmYSsSiaTmpyc1OTkpHp7e1UoFIwoK5VKFmVRrVaVSCRsfaDPYrGYyerd3d2OKK3Q4AvH1I8FnzvPuPHNe1n8NVkbHiR3u1aoN5hDH7bvvbpeluJdbzab6uvrMxLU6we+0y18kvc8SddqtZROp40AOTg46NCLPtzTy5huxpuXZaEs6ia7/Xph/3STY93mivt2M4hDW8iDGu/hZrz39vY6IszYSzw3NmFfX5+lLyGPICbYH8gDxj/0Ansgy9ym02m1222bX2+H+uft1j8/7ueNR7hnvaw+7/eLDO5ugKXbs3nZ5CM3+vr6lMlkND09beQz9hffgbiJx+Pq7+9XMpm0EHbI90QioYODA+3t7XXYrF5P+XHxcxCOnweW3YgUP25+rMJxCG2fUO/773fT1S8aWz++4Rp40ffD+fXrKfzbf8Y/p49q8nsKZwvkEbbCi56/W3/82LxIp3Tr18vu8bIWXu+877/o/a8Kqs97hm5y77y5flF7ZSDsDdRww3gvC0aoz8X1CwuWKp/P6/LlyyYMi8Wirly5ong8rvX1dVPgjUZDs7OzikTOGL9MJqNGo2GGuyQDbrDcmUxGtVqt454+l8X3B+YMzyxC3Yeues+qF54+50Q6C9mpVquamJgwhm5yclK5XM48tevr6+rr69P29nZH2CjAg/DIZDJp4Bc2Fu8cz81G8sqda0nPGahsNmtgHEFJaJxnuAD3/vuw6oyBN3S9MR6JRIxMQIB2M5b8gkUohAwngll6bohInTm5kchZfg+5zkdHR0qlUpZLDVmRTCY1PT2tbDar/f19ra+vq9FodORjMp7eM+iNeJ6ZufEbzQMN9kCj0TBQMjo6qnw+r3g8rkwmY/1BKBKqRAOEMF+Hh4c6PDzUxx9/rNnZWRUKBU1OTiqdTiuTyejKlSsqFouq1WoWsgcj7716vq89PT1Kp9MqFovK5XKWZ+fDZ/FQYbzwd6PRsP16+fJlHRwcaHt723La8/m8hoaGND09rVwup2w2qy+++ELPnj3TgwcP1Gg0tLe31wGkPAhmrXiB7yM1wr54w4Fn7CYQe3p6OjzSECI+zy/cNwBMCJajoyNbs6R0nJ6e6qOPPrK9m0qlTCb55/T7KZFImDHhPRVhWDT993uAvpRKJY2OjnaE1bPeMczJC200Grb3vQHjDUI/nvzuBlhDw5HP4hX14+5JCmRCCIz9+Phrdns/JBVCg/U8Rc/9vLfOe9m73dvneiKf8PZKUiqVUqlU0qVLl4yEOjo60sHBQUe4dmi0dTPoQmPQgwT0EqTV1atXNTk5qf7+fkUiES0uLqrZbKpYLGpnZ8fIquHhYSUSCfX19en09FSJREKpVEpbW1taXV3V/v5+R95lN6P3ZaTDy8ac+Qm9Ln7u/HusyW6Grl97ELMQE4SNh4CO9wEBEE0e4HUzvvx6oyEDksmkCoVCh2eQ6+7v79s+DgFZN+MxXA807yXvBtS6tRcBum6gwz/Ti/72NgWkDKlC1Ebw3/PpERj8rOHj42OLMsJu9HuYde8JD348mZNKpUx+h/1h/4R9CO3Wbu/5MQvnKWz+893W6MvG1s9XKGPR+/QvmUyqVCpZZAgEMlFqsVjMdEc8Hlc+n1cqlVIymVSj0TDSmZoCoXz3ER8vWm8hqPRy3fflZcRCCFq6kQjhuPv58Puym7x40Rx0k13hvbyc4TreeRN+zj8Hr4VrkIY9DbkMEA7B93nPFY5Zt/X0Vdt/KhDmeV/2/W42Q/jdcG+e56l/lWfrtm5e1r5SaDTNCyCf84RixdgjlIWcjkwmo2KxqFu3bunWrVuamprS//q//q8aGRnRN7/5TU1MTGhzc1P37t3T1atXValUtLGxoVQqZSGweH+r1ao2Nzdtw4+OjnaEk2Bs4xnGG+j7kc1mNTQ0pPHxcU1NTWliYkJTU1N666239PDhQz169Eg//elPVa1WraiGL4yDMKnX65qZmVFfX5+i0aimp6c1OTmp69evq7e31573L/7iL7S6uqqNjQ3zCnv2nXEkfEh6nj/a19dnwi6dTn/J4IZBXF1dNW/fa6+9pqGhIRWLRUlnRVQajYZWVlZscZZKJT169Ehra2va2tpSNps10IbyI48HoYmRxibu7+9XsVjU8PCwyuWyyuWyFb2whfZ/G+IUYUomkxocHDSWuV6va29vTwcHB6rX6x2GAH+fnJyo0WhIOtsofP/09FTlclmvvfaaJicnVavV1N/fr2w2q5mZGZXLZVUqFa2vr6tSqRhQZa3G42eFtwAPXsh7QQch0NPT0xG2hYKRZIxrPB7X1atX9e1vf1uJREKff/65FhYWVKvVOhhcbzCxXjEW+vr6lEgklE6ndXh4aPPebDa1sLBg4DwajWpsbExvvvmmnj59qlqtpsPDQxPIAF0A8KVLlzQ9Pa2rV6/qiy++ME8GJILfz4w1/czlcrZf2LfZbFZ/+2//beXzeQvhf/Dggf76r/9af/AHf6BPPvnEvMZe+KVSKWWz2a5kgCdDvLLw3gmek30J8BsZGflSob6+vj4NDg4ascCexmtKg2ABNCYSCZVKJX3961/vyDlrNpsaGBjQ9evXtbm5qbt37+rRo0dqt9tmALIH6DMgFSIKLyL5z4TV0wArNPbMb/3Wb+m1117T9evXVS6XVa1W1Wg09Oabb9qegVCcm5vTv/k3/8YiP0IyEPnowxP9GKO0W62WjTGRIDdu3ND+/r7J+Xg8rmw2a/sI2eSNNVISfH4u+8vvOWRNKPvpn39WD2q89zL0ICO3mA9kGuAW+eRJFz7nDf4rV67o2rVrGhwcVDKZ7Ih24D6sH575RYYL40PIdTQa1fDwsNLptPL5vH7pl35J77zzjmZmZnT16lXTpT09PfrRj36kpaUlNRoNbW9vq1KpqFKpGGDAC4RB/OjRIwuzRmZFIpGO/kGmsY58H0KweZ4RhO7o5qGXZLLPk4jh58KQRvQGeht9vrW1Zc/iwzd7enpsv6+vr+vk5MRAFPIxl8spGo1augnPzB4mwmV8fFzFYlEDAwM6OTnR48ePdffu3Q4g7NOKIEzJ92aNdouQYGy7ATAvB6XnRGk0Gu2QDaERiWz0efisNa4TGvzMgd9TyFBer1arkp6HttNnf132NuvIOxG4B/bNwcGBRkdHLZoJfU6feI7T01Pl83n19vZacc92u63+/n7znKJ3QwKPcfdjxFiHQMqPBZ/zZJ6PHHjZ+vdz0w10eePf6yEiHPr6+jQ9Pa1Wq6WVlRU9ffpUa2trNi/k9oapAi9rkLs+NDoE5SF5wmf8eJwnF84jykIAFIJN7uN/+zGkQbSE4+nlhb/ny56rW/PpOiGZG9qG/vlCEg15wGuQaXjzXwQGw/69DBSHLQSpfsy6PXv4d7fvn9f3kHQL7+dbN9LhZaTdedf3/3sZ2m0sX9T+kzzCvoXKyi/uVCqlTCajbDarGzduKJvNqq+vT2NjY2q1Wvrss8/0+uuva2pqSlevXlWtVlM8HtetW7eUSCS0vLyscrmsYrFo+U57e3vq6+vT5OSkeSHwvPGceIm2t7e1vr6ulZUVM3ojkYiBjNHRUZVKJRWLRQM59XpdH330kTFv3pOKocQiHxwcNJD47rvv6urVq3rjjTdULpfNM7WwsKDV1VXNzs7qhz/8oSnG0IuLkCVczStRjAr6hoHjFRYTfvPmTd28eVNvv/22Ll26ZKFzu7u7Ojw8tJzVVussH+dXf/VX9cUXX+jhw4f60z/9U7sm95Q6Q3190QDAEp/r6+vT0NCQeaA9mMKThgHHGtnY2LBCL3wWIe29rTwPHmzCzsfGxlQsFrWwsKBWq2WeUQD1/Py8NjY2DBwSuogS9BEBISDiPcbAh66GjfEnlDSdTmt0dFSrq6s6PDzsqAo7ODiovb09I4m89wcDhQgHFC8AjfxkPCILCwsGMG7cuKEbN25oZ2dHn3zyieWSFwoFTU1NKZfLKZ/PdwBF7jM4OKhoNGpkiVf6KF4MjEePHlmRqHq9rng8rvfee8/GrLe3V8vLy9rc3NTS0pJ5cDBwMJa8lw1A5pUt+5j1FeaNe29qJpPRwMCAkTKNRkNbW1uWv7e7u6tyudyRx8o9fBRE2PBwj4yMWAEilCHPf/ny5Y4K3qxtQuJjsZgZ3ryXyWRUKpU0NjamdvssJ3RxcdGMPK7jvdIoUEJwZ2dndXh4aH2Yn5/XwcGBVRdfXl629ef754ubEbkTAjfGFflDCHSxWNTIyIjGx8c1Pj6uer2uWq2m3d1dVSoV7ezsdFQmZazwmPuwcT/P3oCGUGTdeGKC9QEBFIlEtLe39yVA7Q0Sxod9xl6j+vDh4aF5cwHwgBS+z3WZB7yQPT09WllZUblc7rhfaDB43em9bHzeh5L39fXpW9/6lhlNxWJR29vbtmbpezabVblc1u7urpaWlrS6ump5hEdHR0YuUi2avYBBhzxjnql7QX2ObiQFz+1l3nnNAzlPDpwXneXHw//NMxCF1N/f31HjwZPJjJEncfmf9ynsBjEI8dvX16dLly5Jkra3t+01TrnI5/MaHBy0IkMDAwMGsNvtdketA9+f0G56FQMtJIb8eIfkSii7QgMx/Axj1o148Kkj4Vz6n/A+IShnTfk6Aj7qy6+78fFxrays2PeZN2/7ZLNZK3i2vLzccUKCB3Rc1wPcENx2Aw58P/TsMYd+DLyRfZ5hHs5JCGa4tpdJ/rP9/f3mNNjc3NTe3p6l/3mZyni9CBCFzRPx3dp5a6/bOotEOkm08H0/LqE89P0NwYsf53DczgPd4TN2u5f/bLdn9WPkx8N/p9v9/R4P3+cHGev3RPiM3fp/nqw4bw2e99nz/u82t2F/w7ns9hn/+1WeI5Q/L9Ir3WTai/rXDau+qH2lHOEQDIfKisnGgEHBTE1N6Zd+6Zcs/5eiMouLi/q1X/s1AzMbGxsaHR01Q/bw8FA7OzsaHx83z0OtVtPo6Kh5RI+OjixkGmUJ0K1Wq+rr6zPjENCWSqWUy+U0OTlpyo68l62tLdXrdWMmfQViDMJkMql8Pq9r165ZDu+dO3d0584d3bx5U7VaTSsrK5qdndXy8rIePXqku3fvant7W5Isx8jnqSHIMBC9skLQ87c/wsYvgkgkosuXL+ub3/ymfuM3fkN9fX1qNBra2NjQ6uqq5QwTwpXP5/Xuu+/a9f7yL//SWFyvXDBSfSg4nhQvxDDcYrGY5Z7ipeO7uVzO+o2XYn9/v6PwAMYLffJCA7a9t7fXPJMDAwNqtVpaXl7W9va2SqWSdnZ2tLOzY2TK0dGRMplMh5HkDX/PGPvx9DnM/rm8cewVJR79TCajVCqltbU1VatVbW1t2drr7+83b4ZXZnjHeSZf+dcbduyhaDSqcrms3t5eK2IyMTGhWq2m9fV1W0/9/f0WHUB+78HBgba2tsyYz2QyajabRhR4IBzmDK6urqrZbGpzc1NSZ8h6t5bP5y183lfj9kZxSDQgWwCC7Xa7w+vg5y4ej5v3jJBQgBdr3d8bUoF0Aw94vEFHnmU+n1c+nzdvCGuF6uWDg4Oq1+va3t42EoGxwqAhdBtiivy5wcFBS+X49//+32ttba0jJx5j3QOJhYUFLS8v2z2y2axyuZxFTNRqNW1vb2tlZUXVavVLe4i9zTMxF37tYxQzvuy5QqGgK1eu6OrVq8pkMqpUKkYGbmxs2PrxIIu5CvMmPQhkbCRZJAQED0Yx8hu5BKnhc0R5dt9n6kawx3p6etTf32/rJRqNGmm6tLT0pcgXD24Zu93dXUsTWF9fN+8/n6f58fR72e8nxoA5zmazun37tn0+kUhYhdFcLqfNzU07fkaSyTny7yFearWatra21Gw2rYAjHn5Sd2hExcRiMTtKxZOsLzLGXtRCWerXNnvcV7UO3/MeXk9Ih2sgJBn8GvAGkdfpfn1B0L/xxhs6PT3Vs2fPlMlklE6nlc1mtbe3Z3UPotGzI60ymYw2NzeNAIP89QA1tJleNl58LwQ1ofHZzRh90fW6NW9X+BSR8Lr++t10Hq970OiBsJclnqgi8mlsbMwq8nNtomPYrxQqxGuMXoAI9ODXkyFh/19mHIeATfqys+e8Mf4qe4Ef+ukL152cnFikVKvVUqPRsPoU3jtPPyGwsD9CgMt9Pfno03DCfnTb8+GY8ln/+7xxCZ+lG9ESfvar7pnzrnUeIfGy73bbCyEQfpU599fDSeCLDfvPhMAzvOd543EekP0qfQ6v8VX691U/c961u8m0l4Hj8+7jccmrtK/sEYaxZiNihKJQUB54OwjZ3NnZUSKR0PDwsBYXFw3Qffe731W5XNbPfvYzFYtFXb16VdKZsb21tWXeDEKifCjV6emplpeX7SiTYrFohu3x8bEZCFSJJL+Ffjx69MjYcphzSVZkgDY+Pq5cLqfd3V21Wi1NTU3pd3/3d/V7v/d79pn/6//6v/STn/xEH3zwgX7nd37HKsLdv39fDx480OzsrEZGRgxYYGyh+H3VP3KBG42GMdbkeTGx5KKRa8AcUdHx/v37loe8v7+vhw8fmqLo6+vTwMCA+vr6tLS0pPn5ea2srHQADb+Y8D57wwTFkM1m1d/fr3Q6bWG9COtqtar19XV98cUX9nm8Wgh5AKHvtzcEfPEfvIilUknXr1/Xf/lf/pf6+c9/ru9973vGzEuy812r1ap5+imWQr/88Q+E7UJ+AOAxKgnJw5Puw90YL0LEqPDd29urn//85+aBAZxxP294eoMeJUZ0AMYxXu52u91xhNfo6Kh5V549e2bPHxohkDDR6FnoPusql8sZeGIPtFrPq1B6w4W1QGVt+uG9bnt7e3Yv1rOvvMw1UqmUarWazenh4aHtXXKaYrGYhoeHzfuGwo9Gz0IZGT8IGAyjTz75RI1GQwcHB7bWCHX0+caEQ3rPEHsykUjo8uXLKpVKdnwNsgmCrlar2XFuhJYzx/yMjY3p6tWrBmxOT88KbB0cHKinp0dvvPGGvv71r0s6S134y7/8S21ubqrdbhtpFI1GtbW19aVQ6VdpzBcABHnhQQT7wSsOomRarZYGBgZsH05NTenmzZu6evWqebCr1apmZ2c7zm+XvgyufY5m6AFBrmcyGX3rW99SX1+fIpGzMwBZfz5SBTIxm83amNM/D/gBvSMjI1bPIJPJaGxsTJcuXdLt27f1zjvvaH5+Xj/5yU/0r//1v9ba2pqdp8x4AKCazaalltAvf4yTJwA8wen/9kVuSPWgv9lsViMjI5qYmNDq6qoVv8I4HhgY0Pvvv69yuazBwUHNz8+rWq2qWq1azQRCJhkD9koqleogRJAVp6en5g0tlUqam5vTs2fPTH8ASJhTP280H9LKe8gqjL92u20e/kuXLqlYLFpoa7lcVq1WM2Dpwa0fa/KxpTMdyMkMeFs82eIrrHvS0YO+dDpt5zO/+eab6unpMbuDYli+QNTGxoZarZbW1tasQrxfG6H+8vuM1s1A81EEocyFiAZEvMzzd54RyD2w4S5dumQytVwu6+DgoCPs2I99SARzPdZ5CBzpJ/KWaA/sRk6hKJVKeuONN7S6uqqRkRFVKhXz+k9PT6tQKNi5w1988YW2t7fNO9xuty1XmTWDjPdV0P24ew+9f2YfVUfUnR9P1r/3+GPDeGLP/+3Hz5ME/hlI4RoeHjZycn193YpiYW9RAJZreIKIMfX99XvHOxGwByg02i183f+Ea4h++DUY9tX/hOuEa/j15QkZ+uXXoL+29DwyLOyvlxkhCRXKkrBvfv69rQt5F8q980Ccl/vhGGBLEtXzqiD6vP3O84cERbj/u+3RF93vRb+7yZYXEU7hevTvn/c8LwK4L+uLH3fvpH1Z+0pA2OcYsnilM48Png+AcKFQMADCcQ6StL6+bt8ZHR3V0tKSms2mJiYmdP36dXvwhYUFY9krlYoSiYQGBgbUbrf17NkzC3nm6B+UPt/HAAAE0/b29mzDecM3l8sZ45hOpzvOYDw5OTHvMsVnPv74YwuvzufzOjg4sHsSflUul60fvkqcZ+YYQ4QZRgv98WFFfM/nUMIkcr21tTWtr69bqOro6KiGh4eVTCZVq9W0v79vYAdP+ezsrIWPo8x9oafBwUELq+MaABWUFh4pbwxRkAvjlHA8AA/j6z3hrDX64w0wcgaHhoZ09epVXbp0SZVKxYq/1Go1qyxeLBYNhHlvq98weNgoJCHJwDGK1RtZGFqwsDyTF5zkhPsju3yeF2HqHIty9epVJRIJ7ezsaH193Tw4jCMeDbx9/pgOIi7wVExMTCiTyVhINACtr69Pu7u7ti846ssXYyPH04ehegDDfAK6+duH2WJEMZY8I2NEhIPPAeNoE3LE+Bx9u3z5smq1mh0N5b3z3rigUJuvKE+lc6/gWV+egJDUkbJATnEymdTx8bHlvPvq6/V63TzGY2NjRtYB6pEHpVJJMzMzyuVyFi1Rr9dtv2HU89zsDx9CzLOHnvFQeXtPVLv9PCcZucOcoFy9sRGGW05NTVnBQr8HK5WK5ufndXh4aPnJ9JVx9N6Dbp4tZJsHC5BIGLmMCeQjBpKXIVRH5Sg3jqLgfXTV2NiYbt68aXJIks3V/fv3tb6+rtXVVT1+/Ng8+ux1mu+PX9M8m5dZ3kDxoZaMtffgUHUYGTM+Pq4rV66ot7dXtVpNq6urJr+TyaT29vaMVPvkk08skiaTyRiAa7VaHTrR/2bdM/esM9JGMpmMBgcHbT+ig0NDJDSGw3nG414sFk2eNptNFQoFDQ4O6t1337Vj4jY3N23sSHdibPnhfQAuYezngbDzjFX6y3iwTiBW0I8+kqm/v1+VSsX05eLiop1i4aNhALHh3vTP5ccy/DtsIRhl/F8Ghj3R5Pe+9DxFCWIYJ0Y8HrfjENEf3C9sodHNa92MXy9fvbeeufRnOVMrAtm0vr5u9WWq1aq2t7eN3NzZ2VGr1bLPc08PVM8DXr4fIVhBZ0L2QhZ5gtrLzBf1398nBCZeb/l6KoD4kEjxeqAbOPHzEf7PeHjy0UcvhOCt27W6gTvfuvWZFs5H+DuUIzxzuOZ9BJHPHQ+fNQSGHiD75wvBnX/PY4luz/Kq4xD2lev61ITzSD8/Zt3GNZyr8977Ku1Vv/eivncb627/n/f5bu1l4Jjf5xERL2uvDIQx/lOplMbHx82oQLkRKkQeMNVo8d7A/O7s7JhXMJfLaW1tzYrwJBIJHR4eamVlxar7NptNVatVO45of39f8/PzBobxHvm8QwwZzx4Ahvy5lD70mMIYeJ583g9gm3CV4+Nj3b9/X3t7exocHNTk5KQKhYKNSb1e7yjOhPeH5/OCCaXm8zEBmNJzj05oqBJSk06nOxYJhVs2NjY0PDysnp6zM0UJldvZ2VFvb6/lOa2srGhra8sAGACX8PJcLqeRkRGrFOm9e4yjz+mhj/44KjxyHsAhrAD5Xhj49xgfFnU6nTYWlfy5sbExzc/PG5hA0QPEvSfKbyAMWEAQIMx7ynhevMI+aoDPQ7RgzNN3SAo/Vq3WWR5zq9XS0NCQhoeHNTg4aAVf8H568CzJ1jjrmbEhJzkej6tQKNgRWzwP4dhEPezs7JgnA08NfYegILSSBhBnPlGo3ijAKxKJROwZuimtUImQg+eVmiQzQKemprSwsGCeKdYIUSHMAQaQzz/1+cXh3IeCFYMDTzjrgnxCX3yMYjt9fX0aGRmxI+BYy0QbQELkcjmbC7yeMM3b29uam5uzVBDWJbLVk0QY78g25BufIUyQPtMXb6x7o6yb0cNrY2NjRp55IFOv17W+vt5RcMznPzPWoReAFnrG/L2Re+wz1qxnd9mPR0dHliuKd4g14ovxXblyRZcuXdKtW7cUj8ftJAJJ2tra0sLCgubm5rSxsaHl5WU1Go0v7T2e04dKn2egnGdw0W9kKzISrw97GH0SjUbtqKNSqWR7cHNz09bX0tKSjVcmk+mog+ENLZ7Bk6qh8YXXs9FoGAnEPITgIWzha+TTTkxMaGJiwlIUqtWqSqWSHfm2u7trOeVUyfUkiZd3/h7sMV+EJgQ+nnDjNdaZl+0QnJBf9Xrd5sWHo5ILX61W9fDhQ/uun1t/fcbX2yDnAbJu7UXG8Itef9G8sH8gnIjygNCDQCGFxD93t/Hs9jt8fv4Ovdzew+pJDU9+EJEQj8dVqVQsuor5R854jyg66Twg3O35PYDy+bpetnYjO/wYeS9vt3kIyRrki48O9J/1gMvbtWF7EVAN++2fISRIu81Zt7/Da4X3Om9Nvghge9s2jIx40di+CtDpNiYh6O3WLw+mXyb/XgZW/b392go/86pA9jzCJbzGec1/7j8VML/o2rRwnLutKS8fz2svA9ahjug2ni9rrwyEURbkxmYyGcViMS0vL5vBCRj2R2lw3iKeNthWcurwIp+enurp06d2Hu3i4qJVt5RkXktfiCqRSKhQKJgnNqxY6DcWCpaqpnzWhz94YIaiBWj09fVJklWN/Oijj/TDH/5QqVRKMzMz+m//2//WPD8/+MEP9PjxY2Ps8fD5AgU+NILnabValquUy+XsKKZ8Pm+h3jD+PqwJI9Xn5xWLRf3mb/6m4vG49vb29Kd/+qdmbB8eHlpYmlc8GGOpVEpXrlzR3NycFhYW9PDhQxUKBWPv8YqQH0cOCwa/9Lx6Mt5jiAbvkUABACYjkUhHZVmMM1jaZDKpsbExRaNRPXr0SD/5yU/UbDaVyWR069YtRaNnhTQowNRqtaw6JddC0eHtYw7wxvscT4xqnoncWrynKBLm5PDw0OaY/zG6I5GzAkmJRELHx8daXFw09v3v/b2/J+kszJxoAqIo2NSeoY5EIjaPe3t7mpyc1PT0tGZmZsyTl06ntbGxoWg0qmKxqN3dXcvrffLkiZ11TQ5tMpk0koc96tcmay8ajZrHjn0GcPReQYxYPMU0FEEkchbGXyqVOkLW2YuQHL/yK7+ip0+famdnp6MSM6kHzDP3a7Va5slEJniQ5tenfyZP3ACAqT7ux5u1cXJyVi2dYiaEaXovVSx2Vgl4bW1NqVRK5XJZJycneuutt2x/1Ot1zc3NWcGpra0tq6S6vr5uQLBYLNpcEBUQHuWFPMA498Y4c+OVhgdDIWB76623dHR0pA8//NBkjM+3Pjw81NzcXMe5upB43ksTAl8/PhBFyFvypslfn5ubs/4znsxlo9HQ4OCgstmsGfHkKgOQOMO71Wrp7t276unpUaVS0cOHDzU0NKRWq6W9vT2rKfDs2TMjqdjbnnBiDRGi6EmHEOz48Fu+09fXp2KxqHw+b2f5AkI4FWBiYkLXrl0z/UDuNdE6H374oRF+6XTaCNFms6lcLqe9vT1L9fDAgP+9xxSCDx25ublpRDUpA6QjeVmNHAJYQ9AgD/L5vG7evKlvfetbeuuttzQwMKB0Oq0f/OAHVhjv/fff1/T0tHp6eqzSPbIVosjrSR+9xRgD6gDOXj6yXtCrHOPjDWwAO1FN165dU71e18nJiSqVinkgfQFLvOfIEJ/v50GTN+C9Yc/Y+/kIDWAf3eE/76/1MgPWgzrWKvIzlUrpzTff1Pb2tkV0IEOj0ahVGqef6Hr/vH5tYzN1AxyesGQusR8gqDc3N7WysqKlpaUO3V8ulztC+SVZmD5jTb0Zrh3my/rxeBFQ9f3wkU9+rvznwr5Go1Hrqwev9NUb6R4QebKTccbBcXJyYulD3daU94aGEQL+WWkeWGKje/kQ6oXw9fAnBByhTqGFwCvUCR6M+yJ63jbz6ysEk+G8dhuLbiATfeUjFMLP+L6Gfej2nZAYCkEaNqVfj16PhPcO+9Ht/xe93u1a3QDqq4LwsIU2hG/h3um2pvz3QhB93nN061+373+VvnylYlkeQOJ1qtVqXypegCcnl8up3W6rUqlYDlClUjFPAjmQMNqAsd7eXu3s7JgSOjw81MbGhuVteY+I9wjTUM4Ibm8Qeu+crzzqFQzgmE3ivckUKRkeHtbk5KSOj4+1vLysxcVFC39+//33rVoxnsOjo6OODeyVI4oej0AkErHwyW9961tWZZvwPs6Vq1arunfvnsrlshlAsVjMKt0+fPhQ6+vrWltb0+bmphmSeHMI78K7CmiiOM/09LSFBnkQLnV64ghVw9vM/EhnR+REIhEzWKTnVYN9+JX3JnFN2GnC7EZHR5XL5awYWblcVj6fV6lU0tOnTzU8PKxcLqf9/X319PRoe3tby8vLJkgprMZ6AERxf+81Zt4x3KlSDXvtC8rg4cGw5no+HB6gL50B3vHxcUUiZ3nUd+/etWiEaDRqpIvfbzDgkjrG+PT01Kpj//SnPzVgeePGDatyDOgj7JfrYAijrDH82SsYhf6864mJCZVKJSOKvJfX54CFytTPs99nzDf95Tv5fF6FQkHr6+s6PT1VOp3WwMCAJHWQXvTDyyVvbHAv7+XnXt5LR78hPVirrAP2p18fpEysr6+bt9Q/VzQa1dOnT7W5ualoNGoh83fv3tXQ0JCSyWTHeeIDAwN66623VK/X1Wg0LDeXo9ZCAySUJaE88bnWjIn3miOT6DtkZ7vd1u7uboeH3EcKMP4+vI694ENuu+kPmvd0e8OfiBpSWrw32xvE6A+emToGrHVy9dfX17WwsKBGo9ERxcH67OnpsToThULBnq3dbtuRU+F4+7XMeqYPnnDw655nyufzRnDhwabvHHuXTqf14x//WAsLC9rY2FC1WrW1w2dJlfDPgYwIDc3zDAR/PVKATk9PbQzb7bYZ6t08Y2HDwCYc+vHjx3ry5InGx8fthIZqtapms6k7d+5obW1N5XLZQtyRNRBxe3t7Rp769eLzCMPQWOaPz7Hv/fFgfObk5ES7u7s25vl8Xnfv3tUXX3yhlZUVk/EY593GNiQ8QgMsNIj5O/wJ54q/u3kffev2fzQatVBvCG9JFlYsSU+fPrW6Lqx77BtqPfT09KhWq3VE2XiSAXvCe2S7jU3YZxr6p1KpWDh2CMyQv+ytaDSqWq1m6w19iQ3iwSHPFhrLXj94HeGJCg8Qwjnit19P/rrYESH56ufVR2wAon0kEnLFE1fh+Pl1Fq4H33//Hs8HYeBt427NAx0PGv24+rV6HqDyzx4CyPNAS7je/frznw/nx9uT/vrdQN959+s2ruF3u819+Fzhegmrofvn7Hbf8PXzZMiLxv1lLZSdL/vsf8r9ur3ur/Uiuen/57VuIPi89fAq7SsDYe/Bw3MCqCT/cH9/30JvMBaXlpasminHmuAtgyWhyBbHJZGvU6/XTSlhlCIw2JCesfcbodsm96DrPKbC5wiw+TEAo9HnVSM5pmRra8vCo589e2bHlgDK+V63TcNnIAHwnlMAJZ/P6+joyHJA+/v7NTw8rI2NDTu314etAniePn2qhYUFLS4udhSpwogH4BKmPjEx0aHgRkZGdHp6ahVVfYVGxt6fr7y/v29GKYIWTyZAgvFESTIWEA/+NfpSKBRUKBTsOBoMmGazad7609NTKyazu7urvr4+pdNpyzPCc+XDAzFwAOmeTGEN+bBbCiphvKK4uT856R6QeEXLHoH4YdwwAigixXVh5ENlKD33rJLju7u7q0ePHumb3/ympS8Qps19GU+v3CnwFI1GbX4lWaEY9htrZ3p6Wslk0iooe6DgAUE3w8GnAtAH/78XcHi18bqQzwxJ4M+p9fuZdeVf83Pl58UrJ8BvOp22I76k5/nR/pr0DTBCHivywa+ZcrmstbU129ecAf3OO+9oaGjIjjWLxWLK5XIaGxtTrVbrCHslPJDn8JEM5xnU7BNPNnmZw9hhsHnD0INjztdmbglhRW5xzZBo4O9uiog59jLAN/IzAdfeqPMhhXj9KZrm82HxlFarVaueDDjI5XId3l7CoVOplO1fDwI9wA/1RFgDwPcxNNy8h9WPGY3Uo0Qiobm5OZXLZSvGBgGB9x3d280Y87orHNtuHhfGgX4iJ/2e4of96tcNc+iBCZ5sznDf3d3Vb/7mbxp4Hx8f18bGhvb39zsKIiaTSW1tbX3ptIYwF5fneVHRGb/ePcjwa5a9x5guLi5qfn5eOzs7HcReCJZ4Nr/O/X7wz+rH+1WM8PDZw8/598P+YkuwlghFj0QiqlQqdiLHs2fPLJULsgsd4UlrX5H9ZaCs27O/6Hl5VmSg1JmPGYJt1hpRb8gpSGFfHI17hnLDA02u73VE2I+XyTC//vEwMg+h57ibAe/liF+Xfoy6jSf/d2vnfcaDRGRlN0Da7Xrhd8Pjz7yeednzhc/zKp8Nv9Ntf3V75vC1sD/dvhdGaXTTUd2eq9u9/DMjm72t+zKg9rI57/b/i+af17oByfPuz3e7Ae/w7/C1btc+D9S+Kmjtdo/z+vCq6+orAWGUIp5aKjgiOAllbrVa+uSTT1QqldRun3mE5+bmzNBmMxEaB9hFGbVaLcvJAQRLZwVBwk56Y8IrJL8gwvxPv6H9tQiDlZ6DI+9B8GwY4OP09FQTExPa3t7W/Py8EQEYaVw7PHqHH7zpbAzYfzweGOhLS0vKZrPmgZ6YmNDMzIyKxaLW1tbMe0KRlYWFBT158sS8o/39/bYJR0ZGzHuyvb2taDSq1157Tf/qX/0r/cEf/IGePHmi4+NjTU5OmpfTh9HRJ4ojeWMJVjafz6u3t1f9/f1aWVkxT8jAwICd6Uu+Xrt9lvsLeD4+PrZqkf39/frOd76j7e1tqz7daDQUj8c1PT2t0dFRRSJnIZelUkmjo6Pq6enR6Oiojo+Ptbu7q/v376vRaJhxzPoAoJPb7tln3/DO4/HgWQnny2azth/S6bTN4fb29pcAF5EK/piqW7du2f3T6bSWl5dVq9WsqBW56YRSs6bj8biGh4ctBPvp06f67LPPFIlEdOvWLQsNlGSFoxDCVEN97bXXtLe3p2q1amkJ9XrdjrPo6+vT1NSU0um0isWivvOd7+jx48e6d++eyQTYei/guynzUMGQi8ie8++xL4rFoqamphSLxWyt9vb2WmGkMLKA3xh25PtBEgGgKBSTSqU6iv1BpvD+F198YaDfGzeEE3Jt+sdRNRTXonr15ORkxzoA1FO1lmdnPJPJpGZmZtRut3X58mU7S5I0BA8++A59Zt95sIhsJEKjm9Hnq/W++eabdv2FhQXbGwAS9r+vrs51QvLB3wMihnB0r9DoE+CSAljeGw9pgxca45d1AOHX39+vra0tSc/rKUxMTGhgYEDDw8O6f/++Tk9PjWjc3d218HbmWpJVsvdn6/oQW29UetDkDXTGGlKvUql8yZCLxWK2HuLxuD777DNVKhWThT6dA+IYohly2q99b5x7me0L8/hTCJh/oqQY156eno58XN888Pb3l2QglboelUrFQtBrtZqFT2ezWf3oRz+ykHSKbEajUY2NjVluKN5/SSY7uYcnG0O96ose8mw+fJaInqOjI33wwQe6d++elpaWbC/SP79OPSj3zXu6vAz0cjjcc8yN9wKiF3iGMLqimwHobR9vT7TbbU1MTBgBJMlIxUgkYrnb/nz6TCajRqNhaW5evpKfDkBmjYUVpelHmKtO86HvpINAPPsoHL+WcBYUi0WLTvNeSkgtv9/a7bal2iBHPBAinB/S7Twyg9f9vHsS188TdgKRWzs7Ox1h0t1aGE3FePoTO0Kw7tdXNw9oSAigZ/FAnwfw/RyGZBJk/d7eXkefvA3odVm3fYJMDAFZCIy7AUXW28uImLAfnszx74UkVxgqjQzwIJBnC23FbuA8dMjhuIG8DZ8v7OuLWrf7vSrwO+874Zz7e7zKd6SXh3R3exZPdnW736v2hfuF8/yy9spAmIeEOUbAhAPCAxwdHenJkyfG3gF8JZmQSyQSHaBvdHRUV69e1fj4uD777DML4fUMN5tNUkeegz//1BtGCGJeRxD6QePZWdxegHINPuOr62I4nJycaGJiQolEQk+fPpUkE2AYEeQ+cR3vffGhxP5sz3Q6rb29PW1tbalSqVho7vb2th49eqTR0VE7kgkwFImchbRSZIVKo4TBYSx6oH7p0iXduHFD6XRab7zxhkZHR9Vut82QHxwc1Oeff66Dg4OOoiyMDxuaowxarZZVkk6lUtrc3DTW+fT0VLlczry7FCkZHh62/DGIFpTe4OCgkQv1el2ZTEaXLl3SBx98oGw2q3g8rtXVVW1vb+vZs2eSZFWsFxcXrXCR94aicFOplAYHB78EKDC04/G4Dg4OtLGxoUKhoFwuZwZCPB636Afvsenv79fOzo7m5ubsGuS9tdttm9PLly/rN37jN/R7v/d7dpZ1pVLR5uamlpeX9Zd/+ZdaXFy08GCIh8HBwQ5PmTeoqLRbrVbtCJxcLmdGKXmEAFCKQfX19emXf/mXOwxYQudTqZQVpqtWq5YXzP4Jww+9sopEnofFAxh8UTbv1SJUTzo7zmNlZcWIh76+PpXLZSNQpOfnTUcikY5oDf/DPROJhCYmJjQ0NGTjAviNx+MdR3AMDQ2pUChoampKp6dnR7Strq5a5AXykPzOUqlkxeSIDpBkxXYI+/S5Z8wp+XqpVEoPHjxQJpOxyIJ8Pq9IJKLJyUnbX0tLS+rt7bXQYbzFHI2DRzGZTGp3d7cjDPg8Jt0b+P39/bpy5YqluLz22muKxWKW6kExJtYce8UrnVwuZ2AGmYz3HKPOzx3XGhoa0szMjBXH8eQR+xOFyTU8KGTPetlNvh1rrF6vG4GbyWQMZPIcR0dHdkYwuoN7+jwu5hFjGtlBX7wcYc1ks1kr/kY0FH1JJBK2r7e2tuwILV9zQHp+9mvYvPEXPiu53dFoVAMDA1bNOZ1OW246a585JV/Xn7jgwT0GL+QXYwVgZ+4BVv5oMYBJPp9Xs9nUtWvXVC6X1Wg0Oo688ycKcG/IJ9YE+trLbvY+uqi3t1c3btzQwsKCnbXsyRTSnp49e2byjYgdvz+8bcF4+3FnjJE7YRQO48M1AHM+pSMskMd9vYwPDVTfGH/2FzUfKCA5Pj6uZrNp9Qeo0cLxSScnJ6ZTqS/CNUdHR23tl8tlA8WQR/TRhzd7EtRHVWBDpVIpk2PMq7+Ot/c86QpxjE4lsopoj+Pj445jF5HzgDY/l7RwLEOCwb8WGv/hXsTeYF8zpx5AhkDaX5t1gz5EZ1KfgMKyzDnPFoJPP55+naKT/Wted3fz8KJrIEz4LI4qZDXNH0Eajm8IsM4DXOEzhK953eNfk87PC/f3Yk9yqo23W/y8e3DlyU3GLATL3fqMHQIJ3k2GdFuD3f5+leafJSRxzrvPV72H//55+yEE1zyP/33e3qN1i3B6EVnysmcK2ysDYX/TMA8sDGdBeNXrdTOypecsoC+UQEjgwMCA3njjDY2MjKi/v9+EhVcuX3p4Fx7jGdRQyHTbaH5CQibTM3O+b95ghEHEA5PL5cyT7fvHtb0x6r033UIkuCehrYQjYlARNs0RPChQWFp/9BPXYDwZE+9F4LxnQjhzuZyOjo7MA+m9ahgzGDR+LBHUJycnxvD29/fbMzLWeNuKxaJVOu7r6+tg+jc2NtTT06PBwUENDQ1pY2NDe3t75oHt7+/X7OysGWbVatXuwbok9BvQ5eeCsOr+/n4NDAxY31C0zAe5s/V6Xfl83oQm4UHMoz+zlzwrAJBfnycnJ5aPeOvWLf3O7/yObt26pXa7rVqtpuHhYU1NTWl6elrlclmnp6cWOubXTpg7yTPgncELSXjz3t6e5cEBmKjUzrjduHHDjHfy+nd2dizPGK8zOZyAL8Y8DHVGyfCD0eJBtC8+4wHs/v6+dnZ2tLOzo0gk0nF8jPe6eWDkPVN+zAFK169f1+XLlzU2NmZGOqH8/qzxUqlkAHdkZETHx8eq1Woql8sd3m8qFheLRUln51dTuIh+IR+YK74L+Ufl8EQiodnZ2Q6ZxR7CiG23z6qKFwoFU2zkvadSKfscVe+7KfXzFA+vpdNpzczMqNFoKJlManh4WFtbWxbxAwni5ZaX0ZFIxHL6AafeYGLdhoZFT0+PnZ+7trbWoS9YR578QXYiZ70c5/kAQvH42XnZHvwQBdLf398hv/r7+zvWozfefUQQryGX/We8LOczAD/OxfaVeQH1yEB/fq/fT+c1xof7hl74SCRigHRgYEDFYlHFYlGpVEpbW1uqVqva2dnp8P56stufxY2nxBMs9BnjPZPJmC5gn1BHgePSACzNZlNDQ0N2v3K53EFw0wdka7iWu61jxgH5TCEsQrGZK6/jj46OtL6+bjUh/PXD+/j5DfcA65Ixo/+sU4AYMhVZQaFCAJP35Pv9eh6ZJT0PraeaOkATe4XUj5OTE/OCs1+62R/YeJ6w904CX/DPz1M32RPKndAZ4kmHEJD5/kLgk3rn54g0PfSKjxQIDWI/jt2e77z57jbuvu/oYeQxhA5yIiSWwuv5NeQJFd4jYsOTg6RLcB1P1HggzL3897vZ1WG/aNiMIdHhoxnO+z56+jxgxrP58f8qz+bvwfdDAiNsfn2GKXO+P14WnAcgXwa4vP73svT/q9ZNjoXPFn72ZZ952b26fa4bIH7ZM73Mbvkq9z+vfaXjk3ggqiiG+TZ+UbVaZ2FOKEsUP94dAFupVNI3vvEN/d2/+3f17W9/W8+ePdMPfvADffjhh6ZYG41Gh7BGSJLfwwJFYPv/YZVDwyU0aNngeFa9F0s6E9z5fN6MmMXFRV26dEnDw8O6evWq5TNyhu3JyYkVj/IKhWeB7YtEzrxZPk/VhwLBFJdKJROsHKdRrVb1+eef25iTC5tOpzUxMaGHDx92eCO84gfMStLbb7+tK1eu6H/6n/4nC+F8+vSpnj17ZmwyRaAODw/NoA2Jilwup1js+Tmpw8PDyufz+s53vqP19XUDpaOjo5qYmNC7776rTCajSqWiP/3TP9XIyIgGBgY0NTWlL774QvF4XBMTE3rrrbe0vLysp0+fKpvNand3VxsbG4rHn+eke2a53W53eIZDQoIKuENDQxofH9frr7+un//85x1nkYb5R1R9JaxOkhkcKysrBlAzmYw2Nze1vb2tK1euSJKF0VerVTMc/vv//r/XP/tn/0yFQkGrq6taWFjQwcGBbt68qaGhIU1NTSmVSunmzZu6e/eu6vW6VYn+4osvzGhCaOdyOU1NTVmV8Vgspt/93d/V4eGhvvjiC/N+5/N5jYyMGACkCne73dbg4KD17X/5X/4Xffzxx7p//75+8YtfWOgcnua+vj7z6rfb7Q4jhHWOQeirhQLuMBIpjoMMwfCWzoDl//l//p+6evWqhWqfnp6qXC7bEVDeA4OckjoLgfX396tUKukf/aN/pPHxcWUyGc3Pz0s6Mx6pgg+wl87Y75/85Cc2Jjs7O1pdXVUsFrMQz1wup8nJSV2/ft2q7a6trSkSidiRVT5ChkgCPMn9/f26fPmyjo+Pbe8+efJE29vbqtfrevjwoaSz6sec99nX12cGLUqE8Gr2X7vd1t27d80ooBALJI6vp+BBQU9Pj8bGxvTrv/7revDggVqtlmZmZqyK7Nramj2HJ5z8XkkkEpqcnLRiiT7kd3h4WK3WWarF6uqqeUekM4U4ODioqakpffjhh6rX6yqVShYxEIIiv6eRRayfYrGoeDyuzc1NNZtNlUolvfHGG6rVakqlUhodHdX09LSOjo60tbWlbDZrHi4ACGAM+eJBd5gr64GLN/zxBjebTU1PT+vKlSu6cuWK1tbWOtJm8I5Fo1FtbGyYYctc4gWiryEgZG6Rb2Fu+PDwsG7fvq23335b09PTltfebDY1MTFhIJBIooWFBau2H4/Hbb8nk8mO446IiMLjHI+fHRd2/fp1NRoN8zry+uHhoW7fvq3BwUENDw/r8ePHOj091fDwsAFx9Ofh4aHu379vJJg3an1hIVJG/FpH3jSbTQ0ODmpmZkb/4B/8A3388ccmp/y639nZUaPRsBQAyGOiHwCvjLX/PgY7sg6w2N/fbzIOD633BrNuvFOhWq2qXq+rWq12pHHReN5uRncymbSj3AYHBy3ahRM76vW6pQscHR0ZqXd0dKRGo6H+/n4LS6/X6+Yd9oB+ZWVFuVzOChdi1xG9gSzAxopEIh0nW/jweeyunp4ei5BIp9NfGhvAPWuv0Whob29Pd+7cUSqVsuO+WCeQdb7q88nJiVVSZ814Mg+CFkLL753QScHrfv2gf0i1gVzHzuKkkcPDQyN2PYHsnTc9PT0qlUodlbBZ1+hFDygZU0/IewKW4oesMdYU0Zndogx83z0BSITB+vp6x2v+OUPyJkzT6EY+ePvc39//Pg84dZsbmifPuqUzeOcI5Cj7jr0aEjre9iUSJSRA/X1CjzV2v68wHhIA3a4TAu5XAaav8plu4+nJo/D9FwHbcI667R3/Hf7u9hm/7iBzXjRW4bO9KkFB+8rFsti0gAU2p9SZMwEApCAQrDDeE4yN/+q/+q80NDSkTCajpaUlPXr0SCsrK5JkC5IziPHAehAQhnB41h7B0t/fb4segYSgx4BIJpPGhuNxoR/cD0COhxuDcmZmRoODg1Y9mmM/OEuV8E7v0SBsxnsxotGoKQf6Rh5eIpEwQ5xxPDg4UKVS0crKinn8hoaGjK3lugg+wtAB3dlsVrlcTvPz8zo4ONDi4qI++uijDs8yxkkmkzFhSjEUn+OH8oLo2NjY0MrKihKJhP7Fv/gXBuLW19dtPMin3Nvbs7yJSCRiYVrXr1/X7/7u7+rJkyeqVCrGnFNozedBEgbK/OLp9AYsz8/cFgoFq8xLpV7WE9cFSBMqCNCu1WrG9FMAJxaLWeihP2IJUDg0NGSVYaenp/X48WMNDg7q93//9yVJw8PDevTokQlVKl5jDFD45+bNm5JknlsIn7GxMUnqCFXq6enR+Pi4HWFC9EWz2TQPOwbcX/zFX+j+/fu6f/++3nvvPRtPX9m2r6/PwAxrtLe3V0NDQ6pUKh1hy+1220LZvfGIccj12dd+f5L/S6E9f0wOe9+H0nlvBcCa/U0YtCTLhUaBHx4eWjg6oDKfz5sRNjs7q0qlonQ6rWvXrhmhARFQLBY1MDCg6elpvfPOO7p7966dy433lnSC7e1tmxs+E41GrUbC48ePValULJqDvUW+OWG+HLfDM6CYMTq5HmHS7M12++xcdA8ivTFycnJ2rvHnn3+umzdvqr+/X/Pz8/riiy+0vr5ungo+7xVPLBZTOp22iuhUcIdcAExgMI6OjpqcJYyR6//SL/2SHj58qIcPH9oYIMe6Ga+AStY7nn0Aa71e18cff2xREJBbq6ur+uyzz7S2ttYRdeONIVJ7WLcYVT7FhnXnI24YI0nmZY/FYvr44487vEJeF/b09FjkRciI+/1Do38cI4hxdXJyYuets/4GBwftCDxOGMhkMpqcnFSxWNTW1pYd2/b48WObD5++hP7DE0itBh9uGolE9Omnn1p/Go2G5ufnVS6XFY/HNTk5adXfP/jgAz158sRqLUCych+ADsY+YwFhjBFFIUWfCkRo/2//9m/rjTfe0L1797S6uiqp00givSX0vCM3uhl03vjzBL23SbwRjZ5nrLw3iLmFaCgUCspms0bieA8V65G/OXIrkUhYygd2DGkckPsYlsiCo6OjDq98Pp/XwMCACoWCGftHR0caGRnpiHqAvJqamrKUpomJCX322WcmowqFgkqlkm7duqX333/fjpfzcwg5CHntvYo8K7+lMyIS8EqIMWPK+iStCh2PzAsBGIDQe0bJJ/feTt9CQ585pzCoJz4kWdg7oJxIiaGhoY6iimH0IfKQddduty0NC/ui2WyqVqsZ8EWeohMKhYK2tra0tramtbW1jrQH1iz61ffFgw/6yp6AAPRkDM/MfiQCw++R8B7+Ph5knQfS/O9uLXwvBOI+EhP94UlV9i/RnP6aPmKTccGZ0NfXp62tLdPpYRRcGMngny8kTMP2orF4ldbt+y+6Vjg3L3rfg9xXvWa3a9DOiyTwqSU4NPw8eHLLA/ewvWws7H6v9Cl3Ub8xEMi8xobgwVh0rVbLPC6cQdrX16dSqaSrV6/aebZra2taWlqyYhVS5yHbYYjYeXmSGCqSOgwnDEUGmII3MPDeOPRMEooL4x1giUHv2dG1tTVtb29bAZ2joyMjDbww9TkgXpnCQjPenhnGiEaYHx4eam1trSN0lnHo6enpMJLJCw7D0lOplCqVihVMWlxc1OHhoRmwFGjhKKz9/f0vVWn0DC7KAA80RdAKhYKi0ahGRkZMsVN1OxKJ6NKlS7YOOB4BhpUCQYxbu902Y9J7gngW72EP2V2/hpgXwspDAxWAxjrwXgK8IBgkjH8qlTKvGUfesIYxTkdGRnR4eKgHDx7oBz/4gT799FM7P5X8JgyEfD6vbDZrxEokErEzvDFoCKsslUqm4FkfrLNsNmt5UoAFziomjPv+/fv68Y9/rI8//lgLCwvq6+vrqAAeiUS+FMLu7+eBAFWLeZ0GKMLIZFw9o+rX0dbWlnmCiUzwYeKe2AiVi2fB8cp7jxrzTSRAu922aBcE8MHBgZ3bm81mFYlEtLu7a55dvF35fF6ZTEa1Ws3CuHd3d1UsFq2YGs2Drq2tLa2urqpSqWhpacmMctJDotGorQVADLIIVh9SwBuTgEs/pp61D5UGxsnh4aFWVlb09a9/XZK0urpq3sLQcKAhN3t7ey3vlrWTyWQ6jBAftTM0NKRYLGaghQiZW7duqV6va3Z21kCun9vQC8oaYK8yl4zF0dGRKpWKCoWChYU2Gg3Nzc3piy++sKJa7A0/TmG4P8/idZInMzGy/GsA8GazqdnZ2Y7rQJIhk6m+71s4X+Eax0jw4bfZbFbFYlEjIyMqlUpWQAmZ5HPKmS/IMdYRe4DCfdJzDxSyh+ZDyDc2NoywAYTt7+/r4cOHVoivVqvpww8/1MLCQodHCN3gi+h5QNTNI8HRemNjY+Yh3tvb0/j4uG7cuKHp6Wn98R//sfWB79H8udCeJApDa72c4TVveHvvLrJPkqUT4G3mSCjmDHnovXje28RaZ80RWulDoNlnR0dHqtVqVpASUogxJDQcAOnBCNeEAAHA0TdvN7D3enp6dOPGDe3u7to1x8bGNDw8rJGREbPD/LpFFiADvUMj/JyX4chb9GM8Hjf7ABDso/1CIOyvFxr0/p5+b4VrLfy8T0nCQPdh2ZDGFNjEdvJ2M+uom0z1YbQA4Xb77LSH0dFRWyvYWdQ8OTk5O4OYfjIm3ItTDF4FYIUEnN8DyC/vrQsBrpeXL2vnPY+/5ste7yYbeT18jnCdeQ+kl//83dNzVoiVqJXd3d0O8qbb2IXA77y+/L/RzrvueQD2q7TwuV80J1/1uuH/zAO6EblH7QbsMk9O+d9fdXy/Uo6wNxDCUADAoc8l5fV4/KzCL6GBg4ODmpyc1K1bt7S5uanT07PKy5988ok+++wzffDBByZAvbD0TA4CEUMJYUE+HwWQDg8PtbW1ZWc4EtpTLBa1ublpbKIPJ6vVauY5WFhY0Ne+9jVdunRJpVJJDx480Pb2thV5ItT03r17evjwoe7du2dAlDNAGTPO64tEIsbkcYYiAGh1ddUABQYtYNrPRSQSMaO10WhYsYn19XWNjIzo0qVLmp6etirAe3t7NgZ+4eVyOavMSb/8MU5UdobFJ0QYjyDeUgx06cyg/M53vmPnqv7VX/2V3n33Xd2+fVt/62/9LXve3t5e8x5897vf1crKiubn5/Xs2TMNDQ0pHo9rfX1djx8/1s7Ojs0zBj7nq3ovJMAEwUbRLYx0DwgODw9Vq9W0urqqarVqoawIdpQVG3N7e9vmz5MweNtR1tlsVjs7OxZiCNmBIt/f39ef//mf2znHhHtzBFe5XNb6+rpu376tSCSiRqOh+/fva2JiQmNjYx3eWIA3x3kRwnx0dKRnz57ZOsnn86rVatrZ2dH4+LgqlYoWFxd15coVEzCffPKJHj9+rK2tLU1MTJgXPJvN2hiTV0iUAtEBz549MyPKG5SxWEy7u7umpHlectXIBfYGkiQ7//L+/fu2/6PRqObm5qywni+6FRo57CFAdjQa1d27dzU2NqZisaiNjQ0D1NHoWRGhZDKper3eYVRQCGtxcVGjo6PKZDIaHh42xUcxGSoOY5wODw+rXq93GLwQU4eHhyoWi0YEcF4s4ALjCcKDFJKdnR1tbW1pd3dXAwMDGhoasuI/kjqiKgAVeM35jPeuhXKdo7vm5ubMu//kyRPzbkMKMq4ewB0fHxvgw4sESeMjc5BpGFBco1gsamJiQvF4XFNTU2q1Wnr27JnlTYdeCIA2EQoeFENm5PN5I042Nze1s7Oj/v5+22tLS0va3Ny0PQpQ8FEK/oxb1gpGK+vN5wUCtii+Qk5sLBZTuVzW48ePbewx1tEHPDuV/pmfEAR63Yv8azab1g8IWoAQxfwglYaHhzUwMKDZ2VmVy2UrWBWLndXr2NnZsfQD5GpfX58Z+p5ogWiALEomk6pUKqpWq2q1WsrlctbPn//851YTYnZ21iJw0um0HYcWjUa1uLiodDqt6elpM4g8sdZsNq3qcSwW05UrVzQ5OWlHjzGed+7cUb1e109+8hN973vf6/AiAq786QwQmESUAEb9nmENshbYayE5S144Rhzyy+eJttttIyJ9HQ4AHXNN1F0kEjE9RmoEc767u6vV1VULXQ8b5ALgJxaLaX19XZHIWQGf3d1dlctlra6u6pvf/KZ5i7/44gtbq+S5p1IpHR8fq1wuK5PJ6B//43+sbDarubk5/eAHP9Bbb72leDyuX/ziF3Z8oa+T4MEH3mDG0INDQreRbZ7I3d/fN1ukUqmYDQeZBgHrHQ80P3+SOqIDPVjyAMfPN+Ppo1IojMj6JIqLUyCy2aztQ2xaX2TM1xgheikWi6lQKBjJKZ2lyZRKJQ0NDel/+B/+B0nS8vKy/vk//+daXV3VyclZgVCKZkLKsP6Ye2x1+uLHJvSgs99D8gb9y771MgtZ7IlYmgfOnqTgu92AVAjoPcj173lnVkgs+Hn01/Wg16dUeVuQueU0h+npaSM1WWs+8tCTex6k+fSacC/4sTuv/yHQ6wYiw7nz43oeaRD+H5IfIdB80RyFf5/3ef9cIaYMgTCOvVu3bpm9tLW11UFSdhuTVwXErwyE8a4BHgjFA8RJMiGEwY/xkEql9Prrr5vRcvnyZeVyOfNS4pH86U9/qnK5bMAAIUloEUahF2osVA+KybdiQKnYG41GrYBXNBq1wiUcGs8Gv3z5soUa/eN//I91+/ZtFQoFra+vK5/PW2EmlN1nn32mP/qjP7Iqt4wVeYEsKsIdW61WhxCcnJxUNptVu33mkdrc3LSx29/fNyFL293d1fLysqrVqoURA4QJ2240Grpx44YKhYL29va0ubnZcQ4ruTaVSkUPHz7U7u6uVaD2IcDkG6OM/eYGOLRarY7qiO322ZFZeCMwDBqNhh49emRzxFEmhGb19fUpm83amcmpVEobGxtmgHuDAMBLOJrPmSR8DDaV77CeUDxUJWftRKNRA9msF8AxxV4AF96L32w2LUz7wYMHKpfLZhjSAMMnJyeqVCrmzfRHGkUiEQNSAwMDunz5snlj7ty5Y8f6cNQLRjAEEwV2YDQ3NzfNO+TD3Ov1utLptN5++21985vflHRWxfinP/2p5XttbGx8iaWXZAQGxlutVlO7fVbYjWqjrVbLAAQkgVcqNIxfZAn3SCaTVqgLAoZqv9JzkMGYeRCMp8CHH8KOc4QbR7L5CsIzMzMaHh427y2Gw8DAgI6Pj7W1tWUCGwAWjT4Pr+Z6HH+DjELA7+3t2Tnqa2trNj68d3BwYCSVD6eLRM5qMlSrVVPEkUjECtkNDw/btXxoP0Bbeh6yGRIFfl6R8azZpaUlMygBloVCweYXhc/3vfHkja+NjQ1LP4hGo1peXra826mpKdtX1WpV0WhUk5OT2t/fVzqd1uXLl7W6umqhZ15Jes8ZkQ88L8+ArO/t7bUj46SzsPRaraZo9CwktVgsWq4rcoOx4JrMtZf7eOKovDs9PW0gYXNzsyOKZXl5WZVKxcg09oP3CHvjCbDm95/fQ4yBlzHI2dPT045w32w2a/eiIrkHUhx1x7m/PCue0oGBAfX19am/v99089LSkmq1mhEB7FMMQjwpPgUCchEdRl57rVYzdp/5PD4+VqPRMIMoGo0a6PMeVemsVgM59RQH7O3t1fr6up4+faqtrS1bB94T542v0APkQVn4WmhIhwSFB1SeNJWeR3nReB7WMOuQeTw9PbW80/7+fkUiERtPQHQ8Htfa2pqdnAEZ6mUjc0FtBR+xxFo6PT1VrVYzYjUSiWhgYKCDiPCRdsPDw6bfR0ZGFI1GtbKyYsU8KXiZSqXMrvBAhDHhmsgpCDLIeEm295hzwCFHa3kyzMsimjfsWYeMJXse+RjKGu8B9zqRzyNT0YveaYFdETpcQsDpicFGo6FSqWTh8hMTE1ZgNB6Pq1AoaHp6WpJUqVT0ve99T2trayaXfDQCa1SSHUHGHDDevj+e2KH/3foNEY8thT3D/vCAyQNVP27+M+cBO79OQnAW7kMPgv1v/7qXQR5EId9J5UTe4Mln3iCzcP743ONw3YV98aSmL2B7Hkh9WXvRd0Jw2O39bn+Hz/+ieQnBefgZ731/lWfxoBhHIlEq6Aqi4CRZ1MpXHbdu7SuFRsNmsgHwciGsCP2QnhergUXEGJJkhjuLEqBGcY0wxCDcXGxyPwCeHQIwIfz5W3o+IZ6x5bl4Zh9WdvPmTU1PTysej9tRPChqgEC1WtXc3JxqtZqNAcoPAes3gWeGGU+ejX55jzA5uxj3kAf+PF5CTOPxuBVlwGPY29trYDGVSlmYJbk2GKbcl3szf4BOGkQIhgSKzHtLDg8PlU6nNTY2ZkZerVbTxsaGIpGIstmshoaGbD4AXvv7+1bhFOVbKpUsxJQwL0BCyGB5AY4y9XlbbE5IFR9O5Q1NBLzPGcTDwpgAKHxuTLlctvBCz6BSaIvPE1FA9WovRMnzKRaL1r+RkRFjxAEk7B1ANiBdknkpMUR2dnY6vBNUzJXOgMG9e/e0sbFhQJLQ/mg02uHZ8yFyrPFYLGZ5q4D9fD7fEV1AXhTEAmumm6AlJLnZbFqhEcCiBw3MPyGpobAFzAAAMPJQ3D4EnbOamXM8L/l8XgcHB3YED/OKnGu1WkZElctlJRKJjpx68h/xPlKl1ytgDE2eK1ToyAvIJwgQ8tAIEURGHx8fm0HkZed5BgWveaPHV8D24EZ6Tnh6g56CSoQ6szbq9brt24ODAyvIxBFiPqQTLwb98+H2GIqMF/nPPsQvnH9/fAfGDHIXA5g8y3Cc/Hh440mSVZvn/hB4nHiAwQuxSdhmo9EwgiU04ri+XxN+f9DCufRGnW+sjWazqe3tbSNLeBaKWAHUGo2Gtra2tLGxoVqtZnOAjENHAbKazaYVCfSygvXhn016Hjbsr4Vs4lmQLchgaj3wHcgUL9uZP65N9FQ8HtfOzo6RxYyt1xmsdd5jzj1Y9t/zMsfvm7B1A3tczwMA72HHi+iBlQdmkJi5XM6qfPNMkE7Sc5nH3Ib9CuUOf3ubA+9lNBrVpUuXOvQh1zs4OFCpVFKhUND29rYRKlevXrVifxSZY+95+4dx8mPGeIXEAq95ozdMh/Jz2K2dB4wZG++lC+fd254eRJ8HHFk/2Jle9nCf8F4eBEAIDgwMqKenR9evX7cTHtbX15XNZq2II+SVj9RBxgHs/Fr2jTF8WfNr5Lw5CvXLy/YJ13gZGPb7B1vqPLB23rOH+97vTf+eJ2BI48QG40hPIuAo/OpJk/C+3cbBk/Pd9LB/Ln+tr9Je9fN+TZ/3/Od99jzw+6LrvAhYh/3FjsLWxlHGvvJypds4flVi4ZWBMJ6Bw8NDqwJKeAUbCuMeY+i1115TqVRSqVRSpVJRNHpW3ZZy+RTzqFQq+uijj4wZPjw87PCsYgRKnQW5ECZ8hnA5zt8FcBEq2mg0rHIphrb0PG+R6oQYu8lk0kKHt7a29Od//udWOAZgAVjEayLJQD5GNZvMG5QYaeT5bm9vq91um7HEZqDi487Ojp15iVceAoLxJ9xmd3dXsdhZcaaxsTEr0sTnCAcul8umxHp7e62aaKvVsqM2qCJ55coVAygrKytmfFLAqN1um1eH3LN8Pq+ZmRnLp1hYWNDjx4+tgqV0prgbjYb+4i/+wkJSBwcHlUql7CihoaEh1Wo1/ehHP9Ldu3dtbClSJckIFISlBxiEYuJNw7AiTNWHi6Go+BvlQgEbn/MHGOB6VKHFCGUNp1KpDm/7ycmJnQ0LEUE16d3dXY2NjWlyclL5fN4qrxNySO4bShaPPECdPYNxj8dzc3PTwAZFs1ZXV7WxsaEvvvhCP/7xj40UAKhjjJP/xdplTA4PDw304TmQztj74eFhI20AQaw50gk8uMKggcjyTKsvRlEsFm1fecXlxxaDA+DoiSJSBHyO/8HBgeWlk1+ezWY1MDCg0dFR9fb2amVlRQsLC+ZZwjvS29trxvbs7Kzq9bpmZmZ0/fp1SdLnn3+uu3fvamNjw2QVOW35fF6Tk5N2fBKEA/1mzfljQsJcbKrt9vX16fbt21pbW9PKyop5eUzQO09umLfNnmAOTk/PzrJlv3jPY7vdNoLDF2F544037BqkosRiZ5XsqcZJVdxGo6FKpaIf/vCHGhkZ0e3bt01G41lfXV3VvXv31Gg0OsLIksmkVWXFo+5zxr2ugDTju8gB1jKAA48vnkbWkPfQQkL29vZqampKmUzGxoUoAYrCxWJndQOq1aoODw/tDG68Q4wz6xQ5hS7irEkIhdBDxVpAT0pnhhVEGQQZ+Wv5fF4TExOWs0tqw+TkpKXXbG5u2lnsu7u7HQQq40aERl9fn27evGnV+8vlsiqViuLxuBVMY30RpcA8+EJW8fjZGe2sMx+tQJQA6UR4SZEvmUxG4+PjKpVK1sdsNquFhYWOAkw+6oZwWuYAGcrYAqKRGZ6EAQygu/nfr03WkX8PXeLBHGsmEokYWOFsd5+TiJwlrzqTyegb3/iGnj17pqdPn1oBM/pICotPI2PNIB99AU/IbYAttsvo6KidPHDt2jVLjfja175mZNXy8rKuXLmiRCKhH//4x3r99dc1NDSk7373u/qTP/kTi1ry6UAeCENesv7pN40xJ/y+3W5bFJ0vmuj3hvcKc5/Q8xqSFIxTGMHjPw/wZV1CyKADPBj2hDrpbugrCpR5otCnh2B/JpNJ/Z2/83d048YNRaNR3bhxQ729vdre3tZ//I//0fbZ559/rrW1NeXzeb322mt6/PixHj16ZGOGPcjzIC/92iLyzwNCvz8gCnp7ezvOI+9GKnHdENx63c64e/Ii9NCG69aTQX19fZZ+4gG4J5B5z/8dkhe+DzSO/SO/fXBwUIODg5bCtrW1pVu3bqlSqejJkycdcqEbeA/BsCfUvAPGAzk/RuHzhX/7dX0ewDyvnUdSdAOo9NHPWTcQHf59HkHgm/8O8xeLnZ3McfnyZfX19Wl7e1ulUkmxWMyikLDDiQYJieuvSh58pRxhwnDK5bJGRkasciCGA2dPEh7C8RkY0gC70dFRM0RWV1dVr9fN2EMwItBhm30ImB9AvwBgJalISt7Z4uKiVldXNTc3Zx7YVqulzc1NUwipVEr1el2bm5vq6enR5uam7t27p1/84hcql8sW6iPJclC9cegrTPLM4UKGzfIhSfF4XPV6Xdvb2xZS5L1ugIhWq2UAF2OJY1e2trbMWCGUc29vT5999pmePHlilWIxaqhI6QkFhInP5QEUjI6OWo7i3bt3O8C9N5RrtZp955d/+Zd148YNlUolRaNnIVNra2t2DNDR0ZHee+8980rPzs7aeCQSCW1sbOj27du6evWq8vm8fud3fkcbGxsaHh42gwbQ74Wl3wSnp6dW2IjcSeaAe/FdxsAX2gkFea1W08zMjK5du6bx8XEtLi5qY2Ojo4o5yhtj1EcQsMmZu3a7bTmyeKkgLPDoc6RUNHqWK4ZBgXLDo4bxQ6GdfD6vzc1N1Wo188AhuNfW1ux+KGbAGYYg6xoghpefczYjkYgV/spkMlYRk2iK999/X5FIxM4IJTcRVu/g4ECDg4NmJPrICcbbr0NJ5tXygJw+Y9D4UHgEJuNOukZYjAhDnLy3S5cumce8Uqno+PhYY2NjRiQlEgnNz89blVdCrr0Rvbm5qQcPHmhubk7Ly8sdUQNHR0eqVqtm3HNGdmhII6u8B5rnZc3icYzFYvrGN76hpaUl9ff3W0oBcoo580V4GCd++z3tAcjNmzctTAxvNl5BiMcbN25YqDve4Xb7+TFmrHdPMM7Pz2t9fV0bGxt2bM7Q0JCePXumu3fvand3157DAz+IKCKLpqenzZPM2gGAeoMD4w+wDUECaMFbG44LsgLipNVqaWVlRb29vbpz544Bxs8//1xvvfWWVVre3t428M/c+wgq6bnxDogFcPv8Q0gM6bmh7p+N132+HoCftUJOFYQhBcMgsjlWi/3uvcV4vwmdRaZevXpVY2NjVscBvYX+hsTFkPY58twbAsBHHjAmvsAjual4tAHkh4eHmp6eVjabVV9fn3Z3d1WpVCznlTH0xBq6gv0cjUYt+gGwiGHtjVzvrfWAwu8dT8Z081SEHnQ+5wtqQohJz6MvIDB2d3eVz+d18+ZNO2bp+PjYCox6wBben3FFX4TPhYykb8wlhQDT6bTNGTUk6vW6JicnNTExod7eXpOLkNfotoODgw4PNM+B3Pdgk+bBCmvKG7z+Wp5Yoj807x0K5Z4Py/b7idfYK/39/eZ1pUYHz+11FuvErwly/jOZjMlBdBj35zPZbFbvvPOOEbSxWEzvvfee8vm8FTCsVqu6d++efuVXfkWbm5uanZ3Vhx9+aLVAcGa0Wq0OLxr99sTBeUDGfw5Aj50Xj8c78tzRP+hXf43wuuF73SJjwvf8df0xjf4Zu4HGENz7OeF5ebZoNGqREH19fdrf39fS0pJWV1d17do1SzWing62MnLTEzMexHaLfiCFiz3aDXy+rL2o7+Hn/D382Hb7TghMvU3miYduz9ptjYX7LvxOuMeRCegMHGr+/Hf2DM7HMK3oVcfQt68EhFHUHN9D1TqUMEe5sOlRQOQ2IKzoqGdWWZxe0SNcfM4NwiYcdJQUXgWOk0DB8Ozeu+Qb1wak7u7umoe0XC4bUKC6KTlWXrF1U3zhpLBZ/IbHaODHLwoMNAx1Qq8xEiSZAeSVBB4YGEHmCPbZhxl6Y4/nTaVSGhsbU6FQ0NDQkDHKCFcAJIYax2PBuOdyOTvWolKp2Fj6fBuOV8LDnc/n1WqdVZp++vSp/S/J7u1DI1AcACSvkDBYOXIK1tv3mznwOVxhiBRrNhaLWX8gWDjqAzDuq/di8GGAUeqdzY1hibefPoWhMxiEx8fH1k9y1LkeY0L+HEB2a2vLit4gSGKxmK1nzl8mt4k9QoieX6ve2044IkILgzubzWp0dFTXrl3T2tqasXfk4EciEeVyOcuB5Uxcv//83mYcUF6sMXKneM69vT0LFSTEmHUcKlaUul9DpAyQIiE9V1YYV+Tae2+/95Qybtxjc3NTc3Nz5n1DqXgyhnxAHwbMc4YGildErF3kKyHn+Xxeg4ODunz5sn72s591ACP2OGspVEye/GFcUEKEJaXTaStCCBBmfXJ27PHxsR2hxtx7kgoZx/ePjo7sTHDGbnl52Yoe+fljXxJuODg4aMfxAYrY/4AJCAe8CKGHDxkeyhY/JuwrQAfHt2EEQf5mMhk7c5ncfoC49xyFxgNEh68QC1D1hoj/Dr+9p4XXMMTZM5ByrDXmz0cHkJPlI5bYk4x3T0+P9efg4EAzMzMmZ0kdaLVadl56o9Ew+cAY7u7uWuFD/5xednt9xJr3a9mn6cTjcQ0NDZks4HgzjkRjzL2nLswr8x5JPsvnQ+PaNz8vzKu3WWjhHPm5C68NiPUyC93daDRUrVbNawUoOTk5sXOB0QPIMp9G5kkx/1zeYG82m1pbW1NPT48GBgaMoD89PdXTp0+taN7o6KgV5vJy3BfrwoANQR/jgwxjDrq1EFSEMtLbPH4cvcF+HtDoBiC6gbd4/KyYH1EgS0tLtp9CgOHnluuHgNPLWZ4TOVIsFjU9Pa3T01Ozp5eXl1UsFnX9+vUOog9SGMLp9PS0I4cS2cXe8XbneUAhtF/9eDAHFPbzRLWfo1BehaCt22//uRCwsjdJZeq2bkMQ1O3H72deI60M+SSdgW0fSTY5OdkRNYSM9R5Sfns9HzY/ln78/p+2bmu22/v+b0/ydYuQoPn951970f3Cz3V7/UXAXdKX0hCJLEJ+gbvCPd7NbnqV9spAmIfIZDK6fPmybt68qRs3blj4SyQSseNbAA2ffPKJIpGInaF4cHBgIcAw86VSqaOYDoJD+nJ1ai8MQyPp5OTsDEzCGHZ2dszbgHBmMzERGFG+f1RexYtHuC9nEWMswI4iyHlur8R43SvzcKJQujTvxZJkXvGenh4zIFOplLLZrKTnBrg3JABirVbLjFPPpkJMoGARboSkUvjqt37rtwwE/+hHP1K9XtfExIQVNMHYI6ynUChYiBXVhkdHR/Xs2TML+eYcPELCCa/q6enR5OSkYrGYNjc39dFHH5lnZWNjQ++9957+4i/+Qj/72c+sglyhUNDExISGh4ctvLLValm49/Hxsf78z/9cOzs7qtVqVsFbkhW2abfbZkBj1BNqybpinv/W3/pbmpmZUalU0pUrV2zNPnv2zDam9/RBYBCOzvqNRqOWE43XmKI9rJ/e3l7LZ+X7Q0NDSqfTHd4+X6wuFovZESLValX379/X+vq6GRzeUMKLBenB0UA+nJzjnADIfO/atWtKpVJaWlqy3Jnh4WGlUimNjo7qO9/5jl577TX94he/0B/+4R+aVxDZwZofHBy0sz29EeqNyr29PcsV4TmHhoZ048YNXb58WUdHR5qdndWnn35qxgTzCZiloejIoacA0KVLl8zjhKFMeC/3peL49va21tfXNT4+bmtkbGzMlMne3p6Wlpb0+PFj6zfrisbY+/8Bxp4hxVsSyhPvRSG8dH19XZL0O7/zO5Kkf/2v/7WWlpbMiIWsyuVyXzqnlsb12Efj4+N66623LPw+Go1aETsANSTHu+++ax4gUhza7bauXr1q1fkpOkZoLHsU8Nxuty18njxzivtEImcRP5A60WhUr732mq5cuaKFhYWOs7HZQzdv3rQKk8+ePbPwd7xcyCFv5PEbGYyBNDg4qKWlJe3s7Gh3d9fCIzl/ulQq6Td/8zftGLr5+Xltb2/b+vN51d4Y8UCYYoanp6d2FJf37LEn2Cfs6XDe0I/MeTqd1s7OjmZnZ5VOp00GkVsICCHdCWAai8UsbYZID84glqSZmRnTJb/1W7+lnp4e7e3t6e7duyqXyzbXntyen5/X/Py8FhYWrE+A9TCcl/sfHR1Zocp8Pq+pqSnVajUrIHX9+nUrhvfJJ59YoT0IX/Q1oNwDE5/yEYJRD8RZ/wBPn8fLmBNt4clDQA5714Mf6ct1TphXxgYjHZD/7NkzvfXWW5qYmFBfX59GRkYUi8X05MkTWw8QxPF43MJffYh7CISRTwcHB9rZ2dF//I//Ud/97nc1MTGh9957z055WF1dVU/P2VGI/+gf/SMzVgm5J3dyeXlZR0dHVjnfh/ejV3CG0EIg7EkqT/oi1z0Z6PcthnIo37wd6WUsNpEnwCORiNlQ7M2pqSmNj49raGhIH374oR0r53OzuxFUrGNSl7we8Gut2WxqYmJCt2/f1uuvv64nT55oZ2dHp6enWl9fV6FQ0OjoqNrttuktbD8KYGErsm55Ll6HlPBEWAhIuwFKH7rM+PmiW2E0igd5XNeTXSF5EX7WA24KxRUKBdXrdW1tbZnjAFuGdRJGE/h14f/3JBSpNrlczlLNVldXNT09bUTt6uqqre/bt29bfYtf/OIXX6o/45vvb2j3h86Xl4FZxiUkGMLxC8m1kFT344PTwI+fdzj55w3ts5e184gor8PCPuAkxZHAUUnFYtFsUYrSst68zvCA/VXBOu0rF8tCqAIU0+m0LZjDw0PzxJJn1G63rVhHu9223CeYXUKP8ehgdKNwABjei+cXEoqFnFMmc2NjQ5ubm7p//74ZZYA8rsURDO3286q30pmnkoH2BVdQdD4UQnpeiCVkoKTnVSH9tbzC5Xp+chFSbLKjoyMDBH19fbpy5YoymYyBCK9gd3d31dvbq6GhoQ4vGf33wgrljgFE7t3g4KBu376tUqlkOV89PT1aXl7W/fv3tb29bV6OfD5vxkSr1bIwotdee03tdluPHz/Wf/gP/8FCKzc3N01gkON7enqqdDptoC0Sieitt95Ss9nUv/yX/9JyWZeXl5XP5+37x8fHmp2d1ePHj1Uul3X58mVNTk7q61//ur773e9qYGBA/+yf/TNtbm5qeXlZ3//+9/X9739fa2trqlQqFvLH2biE71GW3TOvhUJBf//v/30LRf7000/N2Mrn89rY2LD8Ojx8ftP39/d3GEY+Tywej6tUKml6elpvv/22gfvFxUUzLkqlkoXcttttCw0ldwgW/s0335Qk/bt/9+8st4viWoB6DGQAL+QS+Xq879c+BJF05mlG+XHEz8rKim7fvq3BwUHl83ldunRJ3/jGN/RP/+k/1fe+9z39u3/37/Rnf/ZnOj091czMjG7cuKFvf/vbpujZB4RpelIHRUXeJV4fjiGanJzU9evX1Ww2tbu7q88++8wAC941jG2/30qlkmZmZvSrv/qryufzFtGAgVev1w0YkKvHGdsDAwMqlUqWQ0wF9v/wH/6DlpeXtb6+bnLGFxqjf755oMPeB5CH3qSQKGSNHR8f6/vf/762trY0NjZmFcQpkITxMjY2pmq1qmq1ajLIg23GnSPjMpmMGo2GAbVYLGbnJzYaDY2MjKhQKOiHP/yhSqWShSuvrKxof3/fqpzv7OxYSgPPj9yRZLKMfe3DxhhDyEpIz8XFRUmywiWHh4daXl62XFeOyEI/eZlLOHw0+rwYHOeYe2CDXFtdXbVqujs7OxbK/OjRI7XbbVvzf/VXf6W5uTkLCfah3JIspcOH2TL/RFVAKnNaAKceAOr4jgcEGNs+SoforWq1qlgspvHxcSP1MBAg7drtts2z12cYHlRIhUhut9t69OiRPQ8FFBcXF/XgwQMjPqjFwXwCgjHKfUpEt0bxvunpabM5SEfJ5/OSpM8++0wPHz7Uxx9/bGNLhAeGHfobwOANUXI2CeNFR3oj1huA1GJIJBIqlUomTwBNPpoG+4D95Y230GhjzL38I+qC/9fW1qwQXyaTsSinvr4+I3p8tAqy3RNtPJcn3L2Hq1qt6tmzZ0okErp79659nxzdw8NDPX36VKOjoxodHVUul1O5XNba2po+//xzAw6rq6tmU+RyOSNR2Fvoeta1fy4PvtCZoZEferNCEOYB73leef8TVo4GLFDUkiiDWCymwcFBxeNxbW5umozpBir9XotEIra+wrBYdBOFFf3JBhDmP/3pT01WbW9v6/d///c1Pz+vX/ziF0ZuElHiZakHfawJolg8QPX6JFyT3vb1x5Ahn0PwH7ZuoIjXw3Hzn+d1bA3OPJ+dnTX7yK+fbnPvr+PThLCjBgYGNDY2ZsfL8RkcX+vr62YnSWcpWj5FgrXq++H3O69BajFuXveG4/6yFq7dbmMofTlFwO8bryPoA7aPL7wagvluADy8R/is4Wt8lv3GmuT7yWTSCjdCehLNCLF+XvP36rYWu7WvBIQxTFHmVEkmT41wAnLgyMukeiA/KM1Wq2XHMKBo2Uy+SqsvuOEn0k+y7zQCEcUNIA9DsWBCfU4kRoTPVQoVlxeq3kPtJ8K/z3cwdlh4bAq/kbotZIy8QqFgxpIvp0+jj9LzMyoR6J6h8coRbzch31evXtXk5KSuXLliQiYSiVg++Onpqebn5y00iIrW3If8v3Q6beG35XK5Iy+L8xrpPwCI/lKwA8N2cXHRQhsxVn24T6t1VshlZ2dHmUxGW1tbevLkiRkJ2WxWMzMz+q//6/9aPT09evTokXmNKUzDmXAoC78WfQXBePys8MeDBw8s/88XusIj7gVbyLB6A0V6fl7glStXdPXqVTt2SlJHuCnn+qKYUd6SLCwKg8gfwRU2D7oAOBBJ7C32Cs9KhVbpefEq/o9EnhdN29jY0A9/+EMNDQ3ZdSYnJ3Xjxg2999575l0FRBJK6u/v94H3xNBPIjUePnxo4OSNN94wgIQRjiHp+8Q1c7mchoaGLMfZ1zYgl7FcLqtQKBjBRH/T6bTlrufzedtHhLlDhqCgYeW75e15ecZ1wpAl5ox95kk3D/Q5bzuVStm52HgsuCeh9Ky9cKz5DKTgxsaGFhcXzcjiiCeMMxTpysqKrflGo2FHiAHSYds5lkWSSqWSXYsQTLxnjIP3boTeT44/Qz7s7e1pfX3d0gGot8C4sVe4Dmt6f3/fPKcof8bCF2DDO8qzQbpyNN2zZ8/02WefaW5uzsIFMTaItvHgl71P/3zKhD8r3MsPnyLAuc8eNPGsACFf2C2dTpvM5DOe1MUzJT0/bzoej1vqB+Qsz1OpVMxr89FHH1mUz9OnTy2tgM/yfBsbG5bD6EFKKCcBIV6Pot/weu3v72t+fl7lcllzc3NaWFiw6DTG3usI5g2yyXsC2+22eR38/bxO5bmQFRyf5cEtcqper3cckdKteXvG2xCQYD6lgPUqyWp8kKcbiUTsfsw/YJ2xf5GR7QFMJBIxzzAGp09JIKpudnbWiMdIJKKVlRVtbGxodnbWbDmuh/MhNFBZ+/wdyqTznteDNsaKsffX8J8/D4iFdpufL8gO5DcyhnRAH1nn7Syu1e2e7DPvJQfoEW00MTFh+49UHOwLAEBPT4/VrgGoMYbe2+vv4XUGKYP+OT1A9GPnxxBZw5h0i3Dx+iycQ69vwvEJ58LbrUR7cQINKYsQc+zlUIaEdpi/9+npqVXzR2fH43Grm4L+ARy2Wi3Nzs7aaz4CwY9XaP+He7sbODtvvZ8HIkPbstv74d/ngVFsFP/jjy0Lo5LOu0+3vfUiEHxe45roP0h7SWbXeCeNv143DPUq7ZWBsF+Qu7u7Wl9ft7yxSqWik5MTXbt2TfPz86pUKqpWq3r77bfV09OjxcVFY2k5eL1Wq6ler+vJkyfGyBLC0W63DfRRIIh7+5AaBJZXVjxrt5AnSR2HzVMEamRkRPV63Yw3ikvxXZR6CG67hR2EBmzIWHI/PL0sOM4R9oWsEJCEN2GsUZyIXEkEHt5kjE9yWBgL74HkHrFYzI5BaDab+uY3v6mrV6+qp6dHq6urdoYpHqIrV67oo48+0ubmpsrlsh4/fmxhI4lEQoVCQSMjI4pEIiqXy1pdXVWr1dL6+rri8bhGR0eVzWZVKBTU399vR0A1Gg0L5+rp6TEPAMfSQI709vbaua2np6fKZrNm4FWrVTWbTT169Eh/8Ad/oGg0qt/+7d9WqVTS66+/rl/+5V/Wt771LX388cf6yU9+ort371rBm+9///tWoMofRUA+/NTUlI3r7u6u3nvvPSNxxsfHtbOzo0ajYWfJ4hlAcfhiE1z/5OTEPB5jY2P61V/9Vd25c8cA+tjYmJaWlrS2tmaF5zCIyFmPxWIG5qhwODs7q4WFBTNCWEcoaxg4Qg8LhYJKpZIWFhYsXAaPFiQSgDMWi2l7e1vNZtOiDrLZrO7cuaPDw0O9//77+uCDDyzk9eTkRL//+7+vt99+W//Nf/Pf6Gc/+5lVMi+VSmZ8olS8t9OHeiIfPKj967/+awvF/b3f+z2122cep9PTUwNo5PWy7wEQV65c0bVr1zQ1NaWTk+f1DUZGRjQ6OipJGhwcNJJsfX3d9ms0GtW7774r6cxjhUeSfFf2HWOJ0e6NZU9G0G/PYvvx8F4KjCi+B2gtFAr64osv9JOf/ORL8ojK4x4E+Yr5yChkHB7Icrmshw8f6tGjR5ZfyhEu2WxWJycnVuyNSA8qyuLJQL5FIhE1Gg1LYaDqLGHi9+7dsyr2kFF+n/ADACGtpVKp6Kc//anm5+ftTGvGGLngPXbe25vP51UqlSzUmTOAmYNms2nVQn1IsTd80+m0nj17pvn5ea2uruqHP/yhJFnkSjweN4KKaAOvTwHn6Dz23vz8vHZ3dztSV+gThSCnpqYsbJXPQkRD8CaTSQtPPT09tXmByGJ8kE0+b5M1wRFgRL/g4axUKhYm/z//z/+zkXakIh0dHXWch0rFcM7MZe15sNpuPy8klE6nrU+EiaKTBwYGVKlU9PHHH3eQiqenpx15fx5osN49QYUubLfbHWH1PnoELxxEGZXriSLw9kihUFAikdDs7KzliAP8vX3giWhvQ7C+vKGHHM5ms0omk1paWtKnn36qO3fu2H1rtZr1o9VqWfoAue38hEaxJ2MJPd7f37djIVmbfI/1+tFHH+nBgweSZEUwj46OdHx8bEcfQiiT5hNGvaGbeB5vw3FfPwb+OZhfdCH7xKc6+DXlwbOXjVybz3tZkUqlTJfv7++brMvlch0nD4Tz6W1OD+pDUsuDmcPDQz18+FDb29saGxuzqt3YfqSIUVwWwp1GdCM6kqgA1r73qEciEYvuYZyRZ35NeFDhPd7IBeaS/YbTiOuy5xiXcP2H8+BBE7IT+UA0Q19fn4aGhrS9vW1RCHwX29ffx/fHrx9sMo41XFlZUbFYNA/xkydPLFqQ9K96va4///M/t7WNjvJ7yZNufgz5H9nmccF5hEH4zLTzwG+3919EfnlC3BdJJYqMtCs8xGF4+8uepdte4+9wzlmn3gnA3JfLZSuEub293UEqeF3lyZ6v2l4ZCONZjcfjqlarHd4+QNX8/HxHJVEf+rK0tGShecvLy1ZRE49GyB5TmAWPAF4ez2D7zeQnBqOPQfVK0BfSKZVKpsgIe0RYAbjDze+NVdgShL0XOkywV8rxeNxAh2c0h4eHNTo6qlarZYViMLgxYFBq8XjcjAnYSQRGu922/GYACiEOVG3EuCbks9U6q4DK0RgfffSRnj592hHuvru7q3/zb/6NWq2z0OfXX3/dqoJzNFKz2dSVK1fMUN7a2tL8/LyWl5etr95LX6/XtbCwYEbSu+++q/7+fvNOkgtLLjKeZyr6np6eFeIplUrK5/N6+PCheZ3i8bgZY3/0R39k/b1x44YymYykM0/rf/Ff/Bf62te+pmKxqK9//et677339Md//Me2EXt6eqyaYz6f1/LysmZnZ/XgwQMtLy/bmbjkb9VqNfsfb4D39HmCg7XcbDZ169Yt/dqv/Zr+4T/8h1pcXNTCwoI2Nzd17do1O8t2fn7ePHxES2C8jI2NaWBgQOPj4/rjP/5jPXjwQJubmybcyOckAoLnOD09taOaBgcHzYiPx89y5b3Rz3qemZnRzMyMjo+P7azZRCJhUR0c9VEul22N/smf/IkSiYSx2t/+9rf127/92/qTP/mTjvD+0CjyFWOz2ayFwrKerl69arKJEFXCRbkOHoOjoyONj48rk8mov7/fjEpSJJiPSqViZ2APDQ3Z3k8mk3rjjTfsOCoaeberq6uan5+3o9vwiiCTfNVnZAmkVOglYA4wqvg8e9kri97eXgPj4+Pjdjwd3jDmDWDiq2rzO2Tx9/f39f7772tra8vIIhT3zs6O8vm8sebkbw4PD2tra8vC8bnewcGBhfEPDw/bnOVyOWPjUboYmCg++gEwn5+fNxAzPj6ujz76SE+ePDGSDa+rNz6R+4wp8ofqt8PDwxoeHjYSlmMaWPtDQ0OWN0YFcY4+azabqtVqdt+HDx9qZGTE5jgs5tFuPz8v1gNA5MXR0ZHl0FJ7g/PH8fJxvCAVgyELMFRZ86zNYrGo27dvm0drZWXFin1RtwCvl/e+8xtQjU73xBpAenl52QBsu91WsVjsqMgNec4zogu9oernjH3vx7FSqVgoNB5JvESSDJhyPx/aF4lEbN2fnJzYMZDcE1njbRAaBHw0GlU+n9fY2JjlrnmCjefkua9fv24OAUKuIZk8OcrzIQuk5+GUsVhMuVxOly9ftmgBCKx8Pm96FK8w9pb3DhLGGu5xbzQyd/SX/UbffCQF409+uaSOdcTxmVwXWQhRQ70SroucYj+c17wdhlxst8/OKfd2FrUGWq2WeY58iCdrzQMS1iFev3g8rsHBQUs9Yt1IMnlLuhp9QQbQugE976FkfnmP9Ytxzw+nf1CLAZnBc2GnYH8yD6Si4cVG5/Nc7JXzQJjX+YxdCN4ZO086eBLXA6cwMsIDRB/Vwjgkk8mOvP54PG42RywWs9x0KsN3Wy/dgJGfI+6Nc6PVallBOvYaJ28wzp7gZ8z9vg1D3plnPuPBvd+Tfl0w3t3aeQD0PM9s+LqXedjtfl309fWZo62/v99kXLVa7bBZuW7ohe+2lrp5zEMZJMkcO+gvTxYQlRRezxMO/h7++q/SXhkIs2hQMGxKjkwi7w3QR0ElNgOKplKpWJElBI/3jpAsDWhCMCGMqTLsc3lChslvVp8Hw6DxPTwCmUzGctj29vY6nlfqVAReeYfsE/fzHjcfnuQBFsomkUhY8Z9YLKZ8Pm8C3CtpX+mV8HQUFYKC+7BYfYU7lAzP6PMW8biPjo7aWbUYXRQ4o0ABhTgo1oAgiEQiBmSJ5a/X6zo5OVE2mzWvOvH+CD0AIwr86OhIy8vLxr4C0mGEqtWqGZIw2MfHxyoUCuaRBcQR4kF+y/z8vEZGRmxcYPQk6fLly3r69KmNI/PsKwUTCbGwsGBRDNLz815R6hhUfo1gUDGGftwQ+JLM2Gy329Zv3uPzeMY8I+m9vX7tMg58l7Au9jBefqoBs4+oBuuBO+PAPfFGwlzv7e1ZdWufs0RY9/r6uh1bE4lErNIpe557sWbb7bblp0uy/GZJXwJ1BwcHVqwLmUGBL9bYxMSEBgYGLK8d0OfzJAHOGGzeQGPuMMhprNVKpWJ7n6q/7FfPvnuiDE8cQIz+eOOUMWctEWrIOGD0plKpDoOD+fRHNaRSKXuOMEQLsgaSEiMeD5j3ULCeuFc2m7UcTsgXjDqKQGGkStLAwIDm5uaMQOKIOMhC9nitVjP5TxRJNpu185LX1ta+pOw8GPRKHl0EWGLeCLPe39+3yJx2+8xDi0EJqeP3MN9jz1O5m/0VGgXea+QNCJ4RQmNra8vG4jy5DqggxJF17g1dUjo487xQKGhgYEA7OzuqVquanZ211ARJHekgnmwhColaA5Jsrgkv9Aaz92D7cN1u3gQPTlhP6CuMRm8neC+tJ9G9h5lreGOI17yRz/vegA2fibli7Il8Qh75CAKMSwgWZAFj7nVdaND633yvVCppampKV65cMblPAbBkMmkkLTKURig8sidML/B97+adozjR4OCg1YPwJL93SCA3PADy9/LpKB7g+cZ4c/9uIMbfC/IwNIr9Z9rts2PAIIr8PkM+eLKF5+N1Iu4gBPx68OMcPrPv04v+DokJZBwyE1sIWxm7lDXOc3og76+L/uOzoaz38+Rb2Ae/r3m/G9Dw4NjvG09uAaC6ASau7aM1uI5Ph5FkdgZ73q+dbs2/7tMcPBEBIAYUDwwMqN1umzfUzxX97eY5D/8Pgah3Cn3V9iLw6+9xHogOv4csAKd5Lzx2PkfWQSwR4XVemtertm7j5WWvl0ndSCX60e2e55EFL2qvDIRhSNiQCLlaraZMJmMGBMwnXiLpuSf18PBQKysrqlarNqAhK0r+DZ5IAB/vpdNpYwGpXukNC2+0+s3rWSUM05GREU1OTiqfz+vo6EiNRsOMWQAQXnC8CSjS0Jj1YRkINgQxgoD3Y7GzYgt9fX1KJBLKZrO6ffu2VVne2dnpME5YrN6YoFgNc8MY4zlHcXkGFsFNbhkbASAMgCHUk5CnnZ0dC3c+ODjQ3bt31d/fr2KxaMAXoyQSOfM+LSws6PT0LG91YGBA+/v72t3d1ePHjyVJw8PDunPnjubm5rSzs6O1tTWl02ltbW3pxz/+sYVl+fxuCABC5icnJ+1olJs3b+rhw4d2DrQ/H3pqaspYfJRNNBrV8vKyUqmU3nnnHfX396tUKpmR40MziIAgBJQjmaSzTVqpVGweK5WKRQp4IwPAjhHjmc5kMqlaraa7d+/q9u3bZlRz9E673dbU1JQZwCsrK2akw45Wq1WVSiXzjPk8D8I+JdmaI6fs7bff1q1btzr6Lsk8m+xnSBeqKfPcHOlw//59+wweoeHhYTv/8N69e1pcXNTR0ZEVL6vVaurp6TFD0Rsa7LXJycmOUH7eJxwOmROPxzUwMKC33nrL2HMiAVjrb775pi5duqRSqaQnT57Ys2DkEOoIwK5UKqaQ2+22tra2rLq3b0tLS1pYWND6+rqFRft8T6/8IRMBU3hwm81mRwV+gBCyg/zkg4MD8yqRoxePx63wG8VovLIjkiASiRgJwHPRWO8HBwfa3NzU9PS0jbeXZSjxg4MDC28nooXcXI5OI48Ndjmfz2tpaUmxWEyTk5O6f/++NjY2tLKyYmAumUzqypUr2tvbU7lcturqknTlyhUr4PW//+//uxYXF7W3t6fp6WkzjjxT7+U0+xUDCFL24ODAQh85oxWFf+fOHc3MzFjl/FarZWfxDgwM2DrxOsyDqdBQQA54XeGNdEJSOV6Ixnwi/zzgwIBOJpNaXV21EDYidsbHx82wz+fzGhkZ0dbWllZWVhSPx63AH54QSA4MUE4QAERj0MbjZ7nDPq/b71tkLMct8R1IAx9t5Q0aruUJN4g/D4LpYyqVsqJi6HWe3RtJzFE3QMAzedBGegXr8vDw0KrWDg8P2/eRj9wPuXT9+nWLOIvFYpqdndX29rYajUYHOe/1g7er8vm8ZmZm9M4779g52eVy2QqAhV5Zon58VWbkBUc9su49IGRMvD2Qz+c1PT2t1157zbz9kkxGkafKeGFPeDniQbDvZ7gXiIYICRiuxT72NRc4GQACIMylxxakGB9RA3jxpedeOu/U8QAym80aIY3tBfnuSRlPvnTrX7ffobfV35f+EIq7vr5u92Z9+8hGb6d4eeOjELvtB59/HxIP3lPcDVjx/svICJ+HzLh3I0H8vPl0Iho2NnOJJxOSh777eehGjiI/PFD3hABOIeyHcO34tAkPZl8k7z3xxrx48i1srwJkw/HjOyEgZ0yZqxAT8bc/ZYPPeuKT8PdarWb2jS/65mWJf65u7TzSwNt+nmSQnh+zxzoOnZDhOPhneNWx/ErnCCNwpOdekP7+fit0cv36dW1ubhp4kmQ5nigwvISeWWRB+nypnp4eE9p4ggiTTCQSdiahZ7EBOd64ODk5sVBSQNPU1JTeeOMNfe1rX1Or1bLjPgBYzWbTwIRnphH4KE6elZBcwKBXQjx7MpnU7/7u7+rSpUsaHh7W2tqaMbfvvPOOxsbGJMkAiVc+PtSWzXP//n07aqRarZpQljrDbKTnQp+FRF4xBi4CdmNjwwzuvb09M6AoUhCNnp0PHI1GzbD1x5Csr69b1VEPkAF1+/v7un79uqanp5XJZOx4K4pMrays6PDwUL29vRoeHrbnZU7pP/drNpvKZrPK5XLa3t5WNpvVlStXbNwk2YHyeKRTqZQd9eSB5L179yxPZGFhocMwwhD68MMPNT8/bwWyGNdCoaDd3V1Tzp6p9ddgnVOkh/UmSYuLi/qrv/ory5MkpJzPzM/PWz705cuXDSRRgAhjh9ByvO7Mxenp2bmEs7OzmpmZ0eDgoG7cuKH//D//z3V8fKzHjx+bcoxGo5Z744XLwcGBPvnkEw0MDFgu+M7OjpLJpK5du2bkV61WMyEajUb1f/wf/4dqtZoSiYT+/t//+/r2t7+tS5cu6fPPP9f6+rr29vZsvXA/5p312GqdFbjCQzgyMqLr169bqNrU1JQZhQMDA0aaXb58uaMwWz6ftyInrL12+7nnmcJQhOriGSCU2p9hKkmzs7P6t//23+rhw4d69uyZTk5OVCwWrZosaQwAMtYvMgzjxxstyAsA/vDwsPb399XX16d8Pq+33npLY2NjGhsb08zMjJaXl/Xw4UP9/u//vnlekXfsk0KhoMnJSU1PT9v+QGb6sEgIHcYMww954QHH66+/rmvXrml0dNQ88tFoVDs7OyY7JiYmLFVjfX1dqVRKe3t7+rM/+zPdu3dPR0dHSiQSVgm41To7tom+3b59uyNHq9FoWFQGFerZ2x5UIJ99dAbvsS8Y762tLUmyI2QgXRcWFiwvtlAoaGpqSlevXlWpVLIctQcPHhhhAFBEvzGPFBjzz4Uc8N47bywBTLg218QQ6+/v140bN7S0tGSewZGRETWbZ8ekrK+v25h/5zvfUavVsjzEaPQs1+5XfuVXTN5UKhWrc4Du9uQzZBSvETbn1zfjDSBDb6E7AfGAWpo3jjyZ0Nvbq0KhoFQqZfUbmCfGyFfsjcViZqghfwFT3vPDmEIQMmfIHMC3l0U4AlZXV02+RKNROzoIoE0E1QcffKCpqSkNDw/rO9/5jgYHB7W8vKwnT54YEca9uI/3cl+9elUzMzOamJgwHZJOp62Q39HRkZ1tCwimj5AgeDOJupifn9fc3JzJBR8a7IsO3bp1S1/72td048YNPX78WCsrK0ZysV59hErozfFz6b1oXpd4Lx857B5khMACQML10FWAe+5FP05PT03HDg0NWTqVLx54enpqupwaLDRy6X3hQN+i0aiRfRwNGXoIQ7DowYp33gD0sR04UhTyhT0jPbcjQnnGGvHgjr76/vrcdgh5D0b8vPE31+1GXKErPSDCPsZmR94QUeSLyLEXGSN0AOQXcoU9j2OF3Pvd3V0j7H2EqF9r5xGSHlDxGsQfDiIK/fq1HKZQMB4hAe3v7SMRIpFIRw2AUPb5Zw8Bpr+f7x+2BPuS97zN7EmwkHxk7HE80m/sn1u3blnELFG9kAbUVvD97fbc4doJm8d//rMeIIepYaw3HwUEFgoB9YvaVyqW5QU2CpOFx9EjlUrF3gPEUvUZxiUUEuEAefSPMYEyopMYaZ7xD9lEzwDxWiRyFvpDXuTi4qKdcwtoZhPiAed7eIWZNA8mCWfr6emxkBoMb0KNvvWtb5kSJdQuFot1CFpyfsglY+FCGhBi+vTpU5XLZRMC9A/Qg2IBSGO89Pf3a2hoyIxZDwi5FoW26Juvqi3JBKgX3uQm039/jIT0PE/v9ddf15tvvql2u63/7X/73wyMEDGA19R7bj1jyP+MB8b8zs6OEomEFepA6FBkJBaLWRgkZ8fiReOIEfIgfGgxc761tdVRUA0vB/fBIJOes56eiCAcnqrfFHNCWQPIV1ZWVK/XVa/XOwoSHR4e2roifLfVapnnEUM1m81qeHhYly9f1s7OjjHpFBhKJBK6c+eOhUQXi0U9evRI9+/fN0+C92SaoHBh2ZAEKDYUHWQJ4ZsIKfY/wIhKvciAbsyyN4YR5Hx+f39fy8vLun79ugYHBzU4OGj7liN7WLeQA+SGE1bc399vz+BD/7zCIESqWCza2PlzINfW1vTXf/3X+uKLL8wbDFhkX/iQaC8vIC8wUJBrfCaVSlkOem9vrylOPP2AQ4qqMF5+3jyR1NfXZ/n74VmWyFy+x/7BYPJecc8sEzYM2eQ9aijIeDxuBdEgM/b29rS8vGx5rsgOKtE/fPhQ4+PjGh8ftxoEROtQDZo1743C84w1L7tC3QAbHu5bjBVf4Ri55sPHWT/sccafeWe+faqQB2neE+a9Hj7CyXu28bK0Wi0Lt6WgIbKK3OtIJKJqtarBwUGNjY1pZGTE9BI/HB1HvQ6vZxk374H1a4rve70cjUY7jt/w80CUE/32Bg/99cYg14f4Qtb4CC3yNf1rXkZ5A917PpHbvlJ4aJB6YMMeOzk50cbGhkU5sIaoIkvfNzY2TBYScSDJiqaFhjGyyfcbHcsP+5ioh3Q6bYQ9NVxYQxCL8fhZ0TtSlUhHCT1jHqBxXSLNAC9+Lnt6er4kr0NgcZ5Mp9/oGn98S+gx4vvoAeQL4waQ8tfnHugz8h59+Cef5zkg2rp5FAGPfl34ucDOwobE9goBZjgm4Vh5OxUZz97wjgxat/H2BDxj46MgPJGG7RHKTd/Oez183zcPzj3xgczGroUo83MnqQNUsWd9pAfpY0S2QeAxXoxNt/Xn+xN6MhkvP8bd1nM3UsPLjvBz3Ie9HT7Xi8b4RcDRf499n06nOz6DLYOsxJYKU0G4DvpDOou68yH7vkAv9lN/f79F+AKkWZc+DD3ssx/zbvuk2/iHfUdnQBT5efPk8au0r5QjzA9MQDwe1+rqqg3qhx9+2BEqxUB0Cx/xyphB84vJAyzPHvoQJwBlu93u8Gz4sFWYUg8gisWiJiYmVCwW9fHHH+uDDz5QNBq1ojx8DuaKCeG6hE0AbqampvTs2TNjxgmJyufzunHjhoaGhjQ6Oqq/83f+jiRZ2B/KBCay2WxqaWnJckMBJBAIx8fHqtVqqlQq+uyzz0z5wgLBYAJkEcrtdtsqjY6Pj+v111839mxiYkJTU1Pq6enRw4cPNTs7q83NTQvhSqVSGhsb6zhLDS8wXioA9snJ87PestmsgXoqjebzef3Gb/yGhZb9j//j/6i5uTk7GorxoMIumxCAiZL2RgT56SsrKxoaGlIul9PIyEiHkE8mkzaG8Xjcqhzv7++b4KjVanaeNeF8KNHDw0PNz8+rWq12gCeECCkArHH2AEYwzzk0NGS5V/V63YR6Pp83kLyxsWH3x4sCYTIyMmKEE2kBU1NTtr8ODw81MDCga9eumZeHomXZbFaTk5MaHR3VO++8ozt37ujNN99Uo9HQo0eP9IMf/MDOzcXgDQveMJbec0leXLvdtgqx3vsJC43wHBwcVKv1/DxMwgW9UgdQAMbwOrEf2QNvvvmmbty4oe985ztaWlqyNcuxbScnJ9rc3JT0/BzkdrttUQcYR7xGmGOrdVY0Y2lpyaqFX7t2rQNAckzUv/23/1b3799XJBLpAMoYvl64I4MgJRgrb+gyrul02qql8z2Y9sXFRVUqFa2srOjKlSsaGhrS7du3bW8QhuqLMqXTaeVyOSPm8Dizhj3h5EkIPFDIPa84kbkAZl98Y2NjQ41Gw8Y/FotZtemDgwOtrq7avDO3VEb9wQ9+oOvXr+utt97S17/+dSON8HBWq1UNDw935O+z/5D7Xl57HUbzDDjrjdQJD8iQ6RTXarfPKvsDDHzIeLv9vOIx+wcZgOfRp7HwGZ6N8GW8eZ5cYOypJ4B3F1JpaWnJPHbtdtuOWKPa8507d/T1r39dly5dsjXfaDRMD0C+QWay1yD7AF8ewPuwbB/lwngByEPyzBuKHnhghPoIBEAMkR+eTMK4C/ceOdDUjNjf3++oQE6fyTuHfGW/8Tw+JA89G4/HLWWI0x/I4YQkgZBrNBpaXl7uONYJ0tmPB+slm8127N/j42MjO3AIEEHSbreNKCWMe2BgwOp0oH8GBgYMgNRqNUs18IDNE87IJ+6/vb1tJzuEANKHdiI3vSfdG9vec8P3qcoc1nvoZjgjr7gGew6bRHqet4je8KH7rE3AN332Rr0/49xXUe/t7TVCELCAnsEJxHggkwA8HiR5G8HLUb/G2OtEvzA//HhvpB9fT04hA1kDnuxg3ReLRRUKBWUymQ4ixD8be9y/7hv98u95ryljQr9YV6wh9gnADP3Ge9zf2/eQ2P6a5XJZlUpFq6ur5sgICQ0/1p5QCcFXSDiE69CTLp6gQM96ryS/qTXidXk3woXrhuD9vH74eaBAGsQ492JsW63np9Rg63i5zLPyOYhWMJo/vnVgYMBssnq9rs3NTe3u7mpnZ8eq16PX/fr28t73L5yH84A/7/FDP9mj1F3ydrgvZvii9spA2IPAra0tjY+PK5lMan193diyra0tCyGUnodxeMGI8QHjhYcmGo1aWASLxy8s6TmwZfIQTnyWs3Db7bYVeRgcHDRQhYE5OTmpo6Mj/Xf/3X+nhYUFra6uqlQqaW1tzRb30NCQHUkD44vRgpGJp3V6elr/5J/8E7XbbS0uLqqnp8eOuWi1WhocHNT4+LiWl5dNQCWTSa2trZnyJdyPCqgcl0Of8UIQgoyhmslkbEED3vFeEOKG0IfZXFxc1K//+q/r9ddfV7PZ1Mcff2w5m3fv3rUqeRRCk2RK+vj4WOPj46bY2+2zKqFsDgREOp02T/f4+Lgd8QIIfvbsWQfDz7xj/IVVPukHit7n5+3v7+v11183QDkwMKBUKmUs0czMjDKZjL7xjW/Y5vjggw/M0Jqfn9fKyooVw5mcnFStVrNjghBygE+ODPFC169zhD1zgXLmcxzfA+s+ODhozD7H/yAQUQ7Hx8ean5/Xzs6OZmZmLIyEPFaEQSKR0OjoqL71rW8ZgN3d3dWv//qvW1jvj370Iy0tLenBgwf68MMP9ezZM1u3KFsEFoIFYuDmzZvKZrPKZDIaGxtTqVRSq9Wy8DlJtpcikYhyuZwZXIlEwqIv8FzjiZeeKxlAIQCF8LVms2mEAf349NNP9Ud/9Ef6tV/7NUWjUVUqFWWzWZVKJQ0MDFjUxN7eXkcV+vX1dQtpy2Qylvu2sbHRIbCfPXtm51N//etfV7vd1sLCgv70T/9UDx8+VLN5dp4nz+9zBkdGRqySts/7pOBTIpHQ9va2GXg+CiUSidhZ2k+ePDHPK4qf40o+/fRTbW9va3V11YgfwIn3JklnSieVSmlwcFClUkkbGxsdZ9Ly2aOjI/Nc4WVjfeNBOz091ccff2w5k6TAcJwVoVN7e3sml2OxmMljDHLGmSJbV65c0ebmpmZnZ7W6uqqFhQUDfxRa88oa488XtEHx+wItXg5ipCAzOWYJcMuaxwBD96HkvdeW4zUwLLzXEaCTy+Us5YV6F+xvQsgzmYzlg166dEk///nPTa9JsuObqKaPB4soDKpsAzAwQFOplDY2NmwevVFPdflKpaKlpSVJz/MVvTHHc6K72KceGJMnHo1GNT4+bjKzXq9rZWVFi4uL+tGPfmSAA9mCNy30svhCiIxpPp+3tU00AsYPQD6dTuv69eu6dOmSlpaW9Jd/+Zf68MMPLdWDfYqHanh4WL29vapWq0YueRmO3eG9G+g+jutj3TG32BkQERsbGxal4veCD1WUZMQUJALVrdFlyD0MQDyeqVTKzrjHjvJOBp/iwHNFIpEOD6YHbgcHB3r69KmWlpa0uLho58b7lCrvuIAo8zqc/RUCJG9wo8+TyaTm5+e7ei4Z63q9bl4vX0cBENhut43I4j3kCiT3wMCAMpmMpZ6dnp5apXpAF7KSIwkhhSB7vb0JWc2RPqVSyeqIVCqVjmNnQrDryTbmFAfM559/bgQzctkDp9AriYzx4+NlHVGYfkyHh4ftxI3wCCDmMwSAHrB4sOL3rfQ8HJd2enpqKWnsXX8SASc1eFLTOzrQe/zgOGq1WhodHdXo6KhOT0+NACOfnnQ8X6SL8WPteAI49Baj89h7PDvPgNMMMoRnjsfjFlaNTMExRUSPlyc8gx/D8wBx2PgOkSm7u7tmV5IW54kOL9M8Wcn4e9vv5OTE6hD19/dbWqR0VgeGfTc6Omo1JogGJmXTp/T4/R8C35CE8XuDfeNJKVIASRfM5/Nm33FNwPmrtK+UI+xZIgZaeh7bjeDzA98N3XtB6tl8z6JglCA0vZGBwAd4IZxbrbPQZM6q5X8mmaM8OPJjZWXFDoT3OQaRSMTOLaRvKBb6hFHDaxMTE8ZQwpQTxnh4eKhKpaK7d+/apsQrjOCkAqokMwJYQF7hIJQRJIRQ0licsKaMLcqMsEsU98LCgj777DM7+gqhhHHZbp/loRLSHYvFjCRIp9OqVqsGTEdHR01JFAoFC/H2VUUx4p88edJhCHgB6As58Py854U/ub4U7cLrRfgnBke7/bz6H0d3bW5umsBYW1vT6enZmcQTExOmlNl8oYL2z8Va9u+zhjCgUWoIR8gc7z1jza+trdm4VatVUyoQHsfHxxoeHrbxCr1QzBF7pbe3V9PT0yZ8Dw4OdO/ePa2trVku8sbGhmq1WkfkhRdK3th6/fXXTRnAzJ6enmpgYEB9fX3a29uzmgHsGb8OYRlbrZZVJYR990IShT00NKTNzU1TkkSEEIFwfHxse1k6U6pUgafoBQWICHM9OTnRzs6OFVgCcJPGwfrDSPQF55iXcrmsRqNhniLGi/nn+YkqkGSeftYEHi3m0ivIw8NDDQ8Pd3gikJdeHtRqNcuVCo9z4Fn4PnIoHj8rrrWzs2PkGFEThUJBb731liYnJ1Uul7WysmKy10cCnJw8P2qKvGo8Y3gWWTvoBY68oHmiE9mLfGJ8IP94PmQFYNyPuTduIAR4DSOZ8fNsvZcpnjT1YVVcx0eqAGyoeO11H4Ytn4FwwLBjztFRkUikI38dzyr9oK/ci/llDfJMjDPGcCx2ljdbrVY1NzenYrFooJooF8LYvbz0/fUhZqFRwt8APEhcjDsiunp7e60wGicTeMMRWea9why7Q/+R6Rh4GKR4Wf1PNBrVjRs37NSBra2tjpQhxg8PazKZNM+zD9fkGf0ehbDC6ERHYy94UIPM9eSKN77pu5d/6BvmNawlwBrjfj41pt1ud6QzQGphKG5tbZkN4clsb2dwPBxrCPmNnPZh1T5iwXuWPSnVzaj3th1zztrjWbwdia2DXULIvA/zDG1N5CWpA4lEwvL1mUd/KsH4+LiRNGNjY2ZHRKPPo6M4NpQ95ck48tkhYrH9fESTl1c8o5fVp6enpoMgybyNDKhmzUB++/xmP3be7mDvcm/2EmuPhn729/Lrw4OUUCd5wOPJRe9lBVRif6VSKbPPkTP0gWfk8+gBxpy9z3pNp9Pa399XPp9XKpUyYAzp49eT74NfPzw/up1iwNiWRJf5eiHYrTwv497f328FLJkDj3vCcf1Pbd6WwLbEHuS5kBN+35+cnFhK0t7enq1V9BLj4dPGiNJh3QBOk8mk9vb2TL94gtL3t5s97ccfG8bbUeQ/c29I0FwuZ3Y/e5m0EU9+vqy9MhD2LI9ndzwbQofYRD4/h8FkgDFKcF2zKPntlRECkwWEIYGx5YFpNpvVzZs3rVAVrMzm5qbW1tZUKBRUrVYtZIrwVM90Y4A1Gg07VoZ+MsHkRUUiEfMqheE+eG3wNH3ve9+zfgGCTk9PLYyVUGOewYfQ0RgPwkzw/EiyZ8TrDhsUjUbNm4JX5ujoSAsLC3r//ff1/vvva2lpyYxQjCfmAs/w0NCQisWihoeHNTAwoOHh4Y7iH6Ojo+btHxsbU61WU7Va1eLiohlqeDNWVlYMEPIDuwtjy9yyIaXOqoTFYtFy3yjalM1mlc/nTQDE43HLL9zf39dPf/pTO5/YA7o7d+5oeHhYiURCc3NzZvBiDHjl4j0E3gPkjXAPkE5Pz6q97u7uWl7L9va2KRyKmWxtbRkrd3JyYgZNIpHQzMyMEomEFanz+ckobDzceBN2dnaUy+U0MzOjZ8+eGVP6gx/8wML7CoWCFbhCsWLs0K94/OxcxenpaX3jG9+wfX90dGSpEWNjYzo5Oas0/vTp0w4gnEwmbS3h/cXDk8/nO0KZfdgilcEfPnxohWQIE8cowvBfXl7WycmJqtWqKU3C9gEgo6Oj5h3GQCUyBaYWo95XY2UNAqj82Yw+msGHOiKQfV43hIMHYBy/4wE0yiyRSKhQKJiXdHd3V5VKxZQ/4JPPelbagzgftre4uKhI5Ow8U0gIxufg4EATExP6h//wH1poN8/Kuu/v77fr7uzsmMfIR3D44oPkpWJEEq6HQevXGQYNxh/GA8/gQ9O9Uem9d9LznD6UNSF46DGMXZ6XfUuuINf0If7cg7FEB/b391uoNLqB+yCnqTLOXvAGFJEWkky5I5cAvT5Xc2dnx9Z2LBYzxpu1BpmDPorH40ZOQQzxjL7QlQfOnpyAQGSMfPQJoD0Wi9kRW56UA2iyjn3YNWfP8hk/D8jU7e1tk6PsKR+Oz3phrpm/Wq2m1dVVffe739WdO3esyB9ee/Qoc4Icz2azFgmBfmYM0fX0D7LSAxU/RvzvQ+7Zs9JzAIG89QCcdcZ68eOMPGLtYIdFIhHLkeT8c8gEChdeunRJm5ubllrjbTN0r3RGVkO+Igu9l8brbMCFB/TMH3YIn2OceDaAkve8eZuHcW+320YoRaNRlctlk+sQ9RDCfm5jsefFmTjtgHoPyCeAcSQSsbnHluG4Nrymh4eHevbsmba3tzuOlERW5XI5DQ4Omi0K4UA9F6qye5sOuY/8RJ+h9wEV2EW+/gwggOPdCMf3gG/n/z4WlPe4L+POfvLgj+cIf/z8hU4avsNr3t5n/pkLai3gyaOwJPr4PLIAuXd8fGz6jr0BUZLP523t9ff3q1wua2lpyfY06yL0THr84cn4ZDKpwcFBs3k5bhX5Cy4iuhVgj/ODo/4A+tzPe4RDYuFF3t8QJ/E3tjRHniaTSXM2QJIQVYbuR04ThUZVfFIa/fP5PhaLRatzA/EGyY3ugazE3vcprd36RfNYC7vEkw/ZbNbkNOsfuz8SeV4PZmRkRLFYzOzNl7VXBsIerKTTaV27dk3Xr1/X7OysFdNAYGCIeVYFYecnkE4zkT7mG0aDvxGoHiyNjo5aqBXhlhzdUSwWrYKjZxEA0ScnJ1aFlhwVhDPKB0Pan5/lFTDPTljA8fGx7t+/36G0qd65vb1txhHKGBZnb2/PWDJfqInFzL0xdvr6+qzaLZ+BGfUHzNfrdQu7u3btmkqlkiTp0qVL5kn72c9+pv7+fr3xxhtKJpN6/Pix5YoynmNjY7pz544GBwdtwUWjUVWrVQslSyaTeuONN8yoq9VqFiIEqE4kEhoZGVFPT48ZZYR/wSgzdp519uATBpnXfd4urDlMI+z5wsKCeUDJdcAjlk6ndeXKFQN2zWbTogYASXhs+I3w8BuYvyFoPKvljaNGo6HZ2Vn94he/kHTGYgJciVjAgNrd3TVQF4lEdPXqVWWzWW1tbVkfEWKpVErT09N6+PChtra2OgTy3Nyc5R8WCgXdvHlTvb29mpub09OnT9VsNi38jnHFC4qB+N3vfldf+9rX7Hw9ACGEFsD65OTs6BDp+XEVhDSS34yHA4Otv79f8/PzdrY4xiNhlQhfQCOyiLXWbDY1NzdnpAaegkqlYmdDA0gA26lUqqOPKDcEMFEUHCPy9ttv69mzZzo4ODASaGdnR/Pz8x1naksyMiwej1uFbW9Mo+gxFPzagTQ4Pj7WvXv3lM/ndfXqVV2/ft1ChDc2NlQul7W+vq7Z2VkzFhkTT8JEo1EDPycnJ1peXtbx8bFVlmWdAi52dnb04x//WFevXrXKxA8ePLA96olLCEhJJpPYP4ODg8YQs1Yl2b5CxnnD2e9fCETkN9WyWUuffvqp5ufntbm5aaSRl4+pVMqOOMIQpsIoueQYJoSscSoAxBkkEKAE4xg9xJFLft6YR4D/7u6u6vV6BxkzNjbWURwMUqfRaNieJCoHnQhxe3BwYHKy1WoZEGNcI5FIRz4l48meWlhYMDkHUUr/mBN/HrX3alN9F8OZaAp/fnwsFjOdk8lkjLw5OjrSN7/5TWWzWQ0MDOhHP/qR5XBCMiFHWQeMIWARMod96Yk71ghjTQHEWCymgYEBzczMaH9/39IUvMdqe3vbohjwDCNrGDevc/b3920+PaDM5/MmnyBPmD90XaPRMDBDBID3nuNBJJQyHo+b0YkH0xvT3ia6evWqXW9zc9MiXgBno6Ojevfdd1WpVDQ/P69Hjx5ZwTfGsFAo6Bvf+IYkGUEYgnnWnrdVPDAh0odcZQizJ0+eWPQB64e+1ut105HYTthZ2IQAdH8WPIDCeyH9dyAHj47OzuRdW1uzoxLT6bSdZoDtBclDCDU2Bv2enp7W1atXdXh4aEcpVqtVi+pij5NCsr+/b5XzDw4O9OTJE21sbBjgYI2Q4uABGF4wwO7AwIAmJiaMwKSgWjz+/Bg0SHLGj+MBiR7i8z7dj/2HbeNtdYAhBDUNMpnvoYO8o4LGXPuczVarpVKppHa7bUQ+hA5zAJmM7vBkIAAtEonY+CC3iPi8fPmy1eeh/grpbsgbT0Cw35BpxWLRTtcYHR01MAwQI2z98PDQUjG9DXt8fKxsNqtPPvnExp017slY9n8Iyr1dyeuedPPveYIPXIH+hHzFFkPG+siAUqmkwcFBXb582Yh+fwxlLBazPQSBlEgklMvlbH3t7+8bScVRfRsbG8rn80YWh55wH3WAzU7YOXOKwwTZAjbL5XJma3GkJHOQTqctbfdV2isDYR++ShEVzi2V1BGu4AU1xhive08xgitkVKXn5/NJslwv8oBhA1lMLJxo9Kxg0OPHjzU2NmavI3iLxaJNGpubv31onWeOKMjFc/h+ICyOjo706NEjHR8fm2GGEPY5ejCYMPAYX0xoT0+PGWqEOXC8jCQ7VmRyclLf//73OwwyX7UNAF8sFg38oDgwzufn51Uul23+UAIYNChFBMHIyIgZ9Xw2Ho+bJ5BiHTCSCBw8lhjpyWTSWEqAExvDC17PNjLWGBT8JseT0DlJZlixhqLRs7xRQLAPI4QZY92Q/O8LkvBsPkzLA5eQoWNtdFMosGI+ZAhjwnvEfOghxh8hz5lMxgQZChGFy1jhGUHIA2IKhYLGxsasQBiGkvQ8HIrmQTyeSwojIBTn5uY6CpjU63VJz5lHrklOEGsNkD42NmZhNe32WV4/3mkAHeG33ksVKlvviZVk4de+OAch+sgx1kiY+oAXgTnH6KKwE0y9D8Vn3iORiBFx7DcEOPljFFPpZmigKJh7FM+jR4+MEPDFXXwVXh+W6L3BNL4HUcM+8R6pZPLsSDpyOSFj8FjHYjGrfOz3BGuFMWfcPasfjUZtHYQgwXtykJeEGo6Pj9sZtpOTkxaCL0m5XM5yurm2JyEhkJADyHIibzink7M62ZMAoGg0aiHYyET0B8SH10EYhKwT5C9GKXudEDWKKyGrHj9+rMXFRT19+tTAFOsbXQU7T/PGk5dRXl/zGf86ssvPEV4Fb8QS2u9z41grvmaGN+A9GVgqlcyY39vb0/j4uFqtlpFInLvuDSNvL7AnfKTK8fGxRYagf1l3fv4BB0RHSc898Dw7XlOAJwX1fLSGt098pBKhrP65w7kJbQW84j7M1dsE7IvDw0PLbcV4ZUzQT6wN5hMDmBxQ5oF0nP39fSvEBqCA8G42mwYQJycnjRhGrjM+9JOxBIRjv7A28ORIsoJMkEsQkD7XVnruqfNj6ceaPkJseZuNFnrTGDfGhpQZvIvoNfQz43twcKDBwUHziuPF7O/vt5Q8aqCUSiU75oq160lVIh7oM2PkPaDITGwTKiNjH3J+PRFUEH8eXPmCcYCfy5cvGyGxtLT0pbFEFnsbOpQtyA3eR9d6r2838MZe8Q17Zmtry8h/SAnGDNkKeIQUwCEGQYFMZ5zRC8lk0uwh9ADReNLzc3OZa+QA88IpL/l83kg06mpQ5BIHAMAtBLLocKKoPFEeEjahZ/08z2+4rv2aD//39hK4AB3EvRk7PLCeVIQM8M8oPa/Tg63hHVA4IHAkQvD29PQon8935Mwj59D1IfnDvmP9s+5xBkC68Xn0DmuUSJn/14GwX9Sgce/pQBH7DjIZXuhjaCL8PDuDwRGJPA/zkTrD81ik5Bx5YSydeSJ/8Ytf6Jvf/KZVUSUvrlgsmtDDyEEY+JBEgAPsOoICYeYVn3RmxHzyyScW5ky1v97eXgutxmPAdRB0yWTSQi7Iadja2rLQy0ajYedccq5mKpXST37yE21vbxsQRlGxWHO5nBWQ4pgWvAyFQsE8h/l83kJym82mGW6ErYyPj+vNN9/UyMiIhUXjCWTxAaaKxaKi0ah5QqQzbx9zjjG8ublpCskrDx9i6oFkyEL7sENYJNjl7e1tCyWEPYd9xUiCjCGEA8abcfThQj7U2QstDNJQoBFuxOc9Y41RjTDnNR8KgpBAgMPOsampFEpYJoQQVSYByIBJDEVCKjkGiDBi30eeT1KHsXZ6emo51RxTtb+/r88//9w+g3eE8cX4abfbGhkZsb2Cd7Cvr0/T09MaGhrS2NiYAfdI5CzvEaVF9WFABDIFuQCQZHzY776YDh5k3zwrihIESAFucrmchoeHlcvltLm52UGiJBJnR3UNDQ1pe3vbyLRCoaCJiQmNjo6a0U1oOcf/4GFirXsvEt9h/Or1upaXlzuefXp62taPD430JCPPw9qj2EU8HrfCYB60AvA3Nzf1+eefWwgbxjR7hyOR+GFMMOYYR0kGqpgvZJ433FkT7AV/fuHw8LCuXLmisbExDQ0NKR6Pm9GEh4R8RorF+IKLeErJh8xms+aZKJVKqlQqkqTl5WULwc5kMh17AO91s9k0udxut81jFEY+oYxjsZhGR0fNK824c5SU9Jx8abfPakZ8+umnevr0qWZnZztCSf269wCBNeT1L0YrBq4ffy+f+NvLVh++5kNzIZ8hSYnQiEQiRjABkqhiXavV1Gg0dO3aNRUKBe3v7+vp06caHR014otn29jY6Hg2bAjpOTgHpLLvKWzYaDQMqFCVnuff2Niw4xwxnon4YmxPT08tPw69TL88ycE4ew8G84BMOM9o9fuQ+h70j/WDTmAODw8PbW2XSiWTVxjfAE/6xBEyh4eHKpVKmpqaUiaTsXQgjk2CfMRDiIFK9BEnamCvUG9hd3fXnscb3ax7f04s8tQbsqlUSisrK2bT+EJ+7AfkF7rUF4nyhvfe3p42NzeNaPFEBb89iINAODo6suKMGNX0HXuLtbO4uKhLly5pZGREly5d6jDANzY21Gw21dfXp5GREatVsre3Z95hiBeAggcnPoTX2ybYM0QlkG9cKBTsefmhOBp2KF4w5BL5rBRd3d/f18cf//94+6/fxtMsvx9/MylSEiVSVE6lit1d1WG6e/IMNs84rNfG3hhrw4av/FcYvrMvDF8aMGDAa/vCC8/a3p1N893JPdM9M91dnSqXskRRgZREKlKB5O9Cfh0dPk1VV+8ufh+gUFUSw+fzPOc54X3e55wPGkAG/pBZxw/wOoX7Rlf7zGaYBb7s8meds0vd/ObmptlYsq2w4LBJkUjE5N33ZTg5OTGb4mWQ4E9SQ/08QLAHtb0s9/b2qqenR319fUqn01ZmNzU1pZ6eHpuIwrOQbcVvQzd5CjT6059tYgCvly9bt2b/53w0C5Z9sA1oFIlEzL4B+vmkIuybarXa0BcAGfQxHXvN/p+dnZkPgcwT8MLETafTxrhhSgP+GpNc4vG4BbYE4b50EblAL3kA3vup/M43DHue67kDYe804lChTL3RhFIHdY/Mgw/SCMwkGbIMbYKNwsClUimNjY3ZWImpqSkdHh5qc3NT29vbtvGJRKJhVMPCwoKGhoY0OjpqAgAK6gNS75SBnNDBlLoQMpRsEsaQjZNkFF0CBhSWF6BIJKLJyUllMhmjY/b09OjGjRsN6+ypdevr61peXpYkpVIp3bp1S2+++aZ+9KMfaW1tTdlstgFooFtvMpnUP/kn/6SBtgMN/O7du2ppadHLL7+svb09vfPOO9rZ2dEbb7yhsbExxWIx5fN5DQ4OamRkRLdu3TIBoyMc1MqXXnpJlUpFOzs7+tWvfmXf8aUvfclQ7ffff99oZ3fu3LHnoiMdSgSlS/2uDxQ5nB4xxonzGQ1m7tLBjtFGNE9gn7e2tpTJZGw/UbC9vb3K5XKWPeZ7eS/KjKAL5yhUavzNmeHgEijgrEpqQImRN6hDLS0tVhfhs2koQ7oo12o1PXnyxGR9fX29oZkVZ5bs+O7urj0jwY43bJVKpWEmM3Nyo9Go0eFfeOEF6167s7OjF198US0tLUY3Gxwc1J07dzQ4OGhZfMCQ7u5uDQ4OmpzjdCITKDyAADIm7HWtVrPszdHRkXK5nFKplMbHx/XCCy/Y+jD+B/kLmSB8LooZulomk9Fv/uZvNpwH6VzJb29vW1Mzrxcl2V4BMPE9nZ2dlqXx2T5P/2tpaTHdB60nGo1axgaHYmtry56BBi2+TwPr40GKWu283nZ9fd0ypHy3dNGdPRqNWkdISk4A0BKJhDnyXAAUrB+2ACPFXgL09Pb2WvCAHPvOlolEQv/oH/0jGwXjaXBbW1taX1+3jLbv9M04PNahs7NTV65csUChUqloZWXFEOnu7m7dv3/fAEz2gFnjsVjMMr/cN+N4+AyCAOliXjB0MmQhGj1vSvXKK68oHj9vnvjzn//cqGOzs7Mql8tWxrOxsdFQR48OxHHy64rDwboRBKNP/NUsg+kdaZxi5AQKGl1ZM5lMw4xKMqo4H2RlW1tbjf2wtbWlBw8efKqOiwBrcHDQbCN2AwDaPwc6iWwDehQ729/fbyUiyWTSMoaAu7FYzEYdkRn56KOPTC/4jqmAo5RWkBUFeAI0xR4SGEiNc5JhdHiHm730wJ7fT/bY+0PlctlKSMgQejAXu0MGhWxvLpczG02wtrW11dBx3LMmaA5Ktp2MLuUTtVrN6lz39vbMR+Je0Nf4B9K5TzY2NmZA1+rqqiqVSsOYm3q9bh3mYRexBjjJkUjEQFvWEF2ILoWiiiMfZokBe46OjrSwsKDt7W0tLS0Z4IGf6v07WAuPHz9WNptVJpPRjRs3TM9Eo1EDtg4ODgxE+OIXv2hAM2UsBNqjo6OWPS6Xy5qZmdH6+rqk80RCW9t5kzzKEPBR0L8+cwkVn8CLfeLMnJ6eKpvN6stf/rK6urr0V3/1V58KlLw/4W0B9ox9guHBezyIiw5qxpoLqevoIs8MYPQYttP7B319fTo9PW3oYeDZXMlk0sowAM8jkYjRrTc3NzU3N6dkMqkbN27o5s2b5stUq1X19fWpq6vLMr3eF0D30+cCnYluJvsOuArAha7g3/39/VpbW1M+n1c0GrXzzb1eFgg/K7Puzz9//B74rDP+JLaciSj4Kehngk7Ki1hTglIPUHr/m3Uh0QK4hC8HcEAdMkF4d3e3hoaG7Jk8RR/d6tmg/jx7MAF969cIO/d3Hgh7egRBCkGr765HoMHhRKGSNYGWxIPRzp8FpfMXiBepeo86sSGlUsmUPxQTfr+5uam2tjZNTU0ZzQeHw2fAcDZwKnz2iOAeI9mM0ge6QWe0bDbbkO2lezVGFpQCJPT09LShdsPTTODhYygZTeIdBdZ/fHxct27d0ltvvaVSqaSzs/Ouri0tLRoZGbFZgDSqmp6eViaTUaVSMUfC0xmmp6fNYZ6dnVU6nTbEDDDBc/454NQ4FQoFc66Zbdbd3a3p6Wl9+OGHDfvp0UcUuK9J8ciXfx/ZcOnCiHD//f39VjOGEaQ+iYtaxsHBQcsc0MzIy71nOnAP/oCGlETe5wN8Ajcy7VC6Ozs7G+bo+iDJz4mkVT9OpCSjVnEvUJdx2Km7r9frFhDiIBIo+WegHAGnHmOQSqXMUeE+JBkI1tLSYnOgkW+y3IxpICvW1nbR/RcEESQenQCCizJnLXEcpQtjHY1GbYYt1/DwsN0/QBiGh5o0jBef6am1yAV6jv0AfFtbWzNZyefz1pFWktHtoN/ymdVq1WimvtzCs08ALnwgg+z47CvyQgBDUOLRaYCFWCxmMt7e3q6DgwMzYl7WOM/SORjnM78YHC/TPtD2AQH7COCD/PjsM/YBBJ/MB44f487i8bgZVLItOBGAntRCLS8va3Nz05rh+Ew5WUvvuB8fH2tra0tLS0sNgQVONuvOMyKjPANBlXeMOM+sK8EGAZ/vNcGMZewlIJNnS2FPPYjsdQqv4cx6NgmBsd/X0BklAwvAiyzgXHpGRblctqw/jpNnNWBf6/WLponVatUyXmQhAbvRW8iFpyt6/c6FvfPlA56Ky/MDynR1dRkro1KpWE8A5GB+ft7uwztf/vt8dp9nDbMyXufjlOH4ed3qn8WXA/nAXroomwE0xI57+ivPjV/B57E/nhXAZ6KnoYoDGHtby72yTvF43JgpkixbTtYfW4uOhhEzMDBgr6MxlSQDbdFxvA9gy2cifdarWQbNg0EwFdDlHjjxcsT+wjYkiOU7vJ3n32Stz87OrJSL8Yz4tt3d3RYssI/Y9/7+fvt+D0CSuMEfiEajGh4eVjKZtOyl760Q+hOnp6dWfoavHY1GP9UfBf/Ig1je3/KfHfpXfj/4PaCiz0J7n6pZ0OYvH7T5Z/HMKHSBz/4RVMLs8bRzbKxnREnnGd5YLGZ+b71et9nP7Gk2m20Yw+Op5nyvT7ag9wnivd4I5YwADZAqlGf/Xq97fLY3XDvkEt8E/d6shKOZ7mGPOd+AIF7fYb9oxEjc06x234PfrBPrhn/Hc9JFm0w+ic4waRTaAS+P6IHwNTCS/DnxwMVnXc8dCPsLyjGBMLReFCjBXbVaNcQWJeKzYWSHUGBkcPr7+/Xqq682ZJuoKYA6J8noPChTjxgVCgXLGJHhomGVrzciW4dgIwQ4Fzi2OJzQN1EO0BipQRgaGrLDBjLFIRoYGLBngrIAUuppCAADExMTDVkbukvjTOzv75sRzWQy+sY3vmHNczY3N7W8vKyrV69qbGzM0FpJKhaLeuGFFzQ8PKyTkxP95m/+pjo7OzUzM2PdjG/duqVHjx4pn8/rwYMHeuGFF0xw19bWrJEUSDLBFVmkpaUlOwCgm/39/ZIumoCFqHaoKBBsHxCHShwAY2pqygbE0214dHTUUPpKpWJzX3GWmO88MTFhtOpisfipQNijXt5x8M4DQQDBpM/SSOeHGARuaGhIJycn6uvr08TEhNbW1mxUCI776emp1Vb7QPj09FTd3d3a3d01KjdyXi6XNTs7aw1jaKwTiUQaOibj/JFh5n6pX4NmhJPKzFka0jEfe2dnR/39/UavIRDu6enR0dGRnX2aSxDAk23xF8+MXLBmkuw+aVzG7wguARP43ejoqNVVXbt2zdb0+PhYhUJB29vbOjs7s3pdjC4GZnR0VOl02pwrrvX1deVyOS0vL6utrU1bW1taWFhoAE8AGzY3N3X79m0L6JALALJ6vd7QNRknxhslHESCJx/AYmQ95QygAToYenVkZETj4+NqbW3VvXv37HPIwHk6NesNdbqjo6NBT/q9QcY50/4CXWZWJc1zsBlke8hKjY2NaXh4WNPT0zaPFiefEXME8dFo1JgeBJKe+hmLxSyA48xQv+5B1I2NDa2trRljgXMd1u1Ho+fdysnIYZBxwv3aYBs99fTatWtaXV01II6zub6+bgEi1Fi+27MMfKDiHVL+j/6hbooSFbItPsODM8XescfIHqUzPkNQrVYNjEilUqrX6w1z7nFe6diMXgcU9XqUQA/nnTIJ30QnzHKgQ/1zeKCFjCPy0tXVpZGREb355puW4ZYuumRns1m98847DTONOROs7bMCYR/g8jOCTf/aMEPDFT4H38/ze50TjZ43pUQv0PgK8MJ3JwbM9p3d0fXoB9Yf8IVgEVCEcgCCGwKoev2cjcAMeUZHxWIxy3Z2dHRY40mSC56p4un1HoTjGVh31hf58n98lgd/g4AH2fasn3C/IpGI6X4aneHM836f6WIvOAO7u7s2gqy7u1s3btyw5/I9OWg4SkMosmRhVhSadCx2XkpBltfvCSABwOfZ2UV3YIIyEkfcB/6I79XCPvrEgl8zD+54XwtdwXeTofV1uewn/hyf56/wTHt/z4NcUHSxq7wGH2JwcNDOQAgO+ixhKpUyVg5NAjkfnNmBgYGGCQCcQ3wRzqlPcGC/sCded3DxfPH4eZ8QPzbQg1T+8mtyWTDsA2EAWd9wNvSnfZDu79UDi/gm4fmhjIm4x6858kgwjZ8pyXpChPfC2sMeArQJWUlePr1OIKHB772uxdbzfPgJf+c1wv6iYY+nKhFMrq6uWvatXq8bpWR7e7thATBa0PCi0ajeeOMN696aTCateciDBw9sMefm5hSJnNcRDgwMmABSg4XzgmL9q7/6K0Nuent7jRLq5+qSpca5wEnd39/XycmJBcT1et0cBpxMDuY3v/lNRaNR7e7u2oE6OzvT5uamNZ4iI+07f3Kw+F5mhyaTSY2NjZmjJF0MeV9cXDTa5cDAgBYXFzUzM6PZ2VnV63VlMhnrbEsd987OjjV1YMQO9LCBgQGjh2Aoc7mcnjx5Yp32oJU/ePBAMzMzlln7i7/4C5sVhiEmq48Dx+eDgs7OzmpjY6PBiSM49ggxQEMzBYqQQ59JJBI2SunKlSu6deuWMpmMNjc3bYbgr//6rzc0QCFD5INZ0FQOuK/nwqkGBCEQ8Zkxn3XiEPJ8IyMj6u/v1/j4uCYmJnTlyhW98sormpmZ0dLSktEkGackyWoiz87ORxOtr69bptvT6w4ODvTJJ580ODDU0J+dnenhw4dGp6LV/ejoqAEq1LUPDAxocnJSL7zwgmX4V1dX9eKLL36qU+Lm5qbV4ufzedVq5xRg6n4nJyd17do1u9fNzU3rYhmPxzU4OCjpYuTO1taWisViQ/2JJMsi4+zhkPlzdvv2bcsofv3rX7cABeeALJAP2sm8eLAjFotZjRw1+JxRhsWvrq5qbm7OHAya9MTj5904Z2Zm9ODBA+XzeU1OTmp4eNgCRfZrbGxMU1NTmpiY0EcffaTV1VU9fPiwoRYGOa3X69YFt1qtWtdzdBFGnIwEdCyyL9RItra2NjQr2tvbs67HGDDpwhmH7sUacD7C+jLOMeemvb1dExMTJgfQoairrtfrRlVl7d98800bz7a8vGwAFzKOzGEEKbfBSctkMnbvs7OzWlhY0IcffmiIM6Ux3d3dGh8fVzqdVjqdViKRsLEQ1BhLF3RKnFj232eAW1pabDRSJHLROAsHvb+/X9evX9e3vvUt/ft//+8tAwxSDiUQ++lRfQ+I4MDjCPhMnDf6dPi/fv26AQ50reaMn56e2lxMutsiv3zX4OCgBabZbNacRRxcZBJQjQCZs/Hee+8ZoDY3N2fOC7bHN32D8UT2P6yhJqg7PT3V8PCwhoaGdPXqVau9TiQS1jMEWvb09LQ6Ojq0srJiJU5f+MIXrNNtIpHQj370I1sP9LzPVuNYoffZF3STD845H/hFkkxW+AzOC3vLecLxpuQAgKtWqxnwQtMe/J2dnR1rMgZgSYkAOokslAdTAZUAIaDa+kxlLBZTuVw29gNOMc/sGRFkX0lgAPJSH14ul62DdGdnp8bHx7WysmI+DY44bDXp0+NVOO9QhpE99m1wcFAvvfSSUqmUJNl89729vQYQyGerfGDtM5Jc7AFsAzqS+6611D5vbW2pUCjo7t27Ghsb09DQkCYnJ80HPTk5MbppPB5XuVxWT0+PksmkMpmMyRpzWOl2j64jqQNYRr8Y7sGzsNAPYbaUYNkD95SjEPygtzwIhT7g9x7gQOfSWM3rGAIzX+YVglq+NIif+8DTg3S+OdPp6anW19cbeoAQyJFoa2lpUTqdNt99fHzcmueyXgcHB1paWrLPSKfTDaAya+lZotg75JFklg9ePRiGb8LZ4HUwaLwuCOWd3/l/I8eUPHAv/J+g0CdyvEyjb6LRqDXE8vIDDZrv4/OOjo6Uz+e1u7trQKtneniaN5lv9pDvw2er1c4njPjMu2caeJAQu8j683M+0/ve/t++XIV7+6zrc3WN5hofHzekWJIptFjsvL08gRCNMUBycFw4nPv7+zZOJ5FIaHx83Lq0Li0t2QKCkHvUAUoAzp6nRWC4yuWyHj9+bMJDdsO3jkcQyfhiAAmEyXT7DBTfScA5PT1tw9ihW1A3Ai2wvb1dy8vLpuD85kWjURNCsqY4S94wEEwWCoWGoB+n9eTkRDdu3LDDAD1HOqeLghYSuDI24u2339bc3Jza29utIQwt4amtJItWLpf1i1/8QsfH5yOn6ASJggRh9gJ+dnZm1McnT54YjbFerxsND+omCD97ROYTpUyAcHZ2ZsAASp3mRv39/fYzDjOONDKFEwXo0dXV1RD44fRDf8RxQbZwJHCWURw4tsgcBxMHsFqtWkv51dVVnZ2daWNjQ5ubm5aNRqH4pgtkeE5OTrSwsGDBJZ9dr9dtPAdnkmwAzwitFkRxbGzMnNRIJKKJiQnrpjk0NGS0K+kiK0UmPRqN2t51dHRodHTUHLCxsTHruujRYZ9ZD6mA6+vr2tnZ0dHRUcOz+8y7X+NUKmU66ODgQK+88orR4z1lkrNAB1YQR09/wpkkaKRmDX1zcnKifD6vXC6n1dVVra2tWfaeGcg4lDi+ZC2XlpZULpc1NTVlju74+LiN4unt7dW3v/1tbWxs6K233tLs7KzRfru7uw0UikajNgbi6tWrDV1a0a+Tk5OmCycmJszw/PznP9fw8LASiYR2d3dNpzAmBv3u0VVPiwYsIohjLzhD3AOBrG8sQ5COfGKwfSdnJhBgK7ifer2uqakp9ff3a2xsTDs7O+bUQZXe3d21MxaJREx+YG9Qb1utVo09tL+/r6mpKb300kv6x//4H+uv/uqvGuqg0ZnM3WxpOR/1RUNCsjbxeFzZbNbsE+cGqt3g4KDGx8fNCUCuJDXQqPlDHR6BgrcN2B/sHmcNUBUmFZ1sWRPkmg6+OObU0/v6MLIgZAETiYSy2ayuXLliM1ZxSE5PT202+dnZmVHYsUPIla8TY1pAW1ubJicnG0oP0DMEcjh3HiiYmJjQ8PCwpqamGkb0kFEDYAJ0glYKe4l1ofO4p/XyfgB0n333zim6iMCW/YICiayFAQXyDAMCIADqMY63dFHiwJ6yT5TW+Bpi7pcmoJJMzwOE8KySjK0GXZ37Pjs7szmdsDioheUi8IURgA7nc+LxuDY2Nhq6HSMT+Hezs7Ofytr6Ncdv8GAD+tWvJzpvamrKOspKMrsAy8Nf3hYS5MCaAPTD7jMrle+Bjkumi/PlAdRarWZjNLHLrC1Z60ePHtmomrOzMwva/QggD5L4mnXsPWszPDxsZwZf3JfiICckcrA/MDG8rHKxzn69/PohB9TldnR0qFqtmlwlk8kGfcbestf46J514b+D0adMt4CBxvpThsVz+kDHg5fYMNbQlyIC/AHE4zPgDxLrMI7JxwbeH/GTbZBT79eHdtKzQ5oFq5ddYZbYxyLYB6/Dwn97hgX+AXqH+8PeM36NdcZvRYZgAfigO/x8/u+zsR609aP2PCPR+9F+XT1g6P1t/1qfVPMx0995IOyFdWxsTIlEwpwMDlsikdDU1JSmpqaUTqf1+uuvKxKJ6P79+xodHbUFwdEtlUrKZrOG2sbjcUP2EQCPYngHGqPlD3cYCEN/wUlBqYCu4XiABIHutrS0WCMcPp97J+NBMxYQauqEd3Z21NPTY4or9f/GqESjUUMoPTLH5/X09CidTiubzdp6hheO3MbGRkN2hGCiUqlocnJS0rlypDkMDihOETN119fXrXFST0+PpqenzbmEWo7y39nZsbrB9957T/V63SjvIHfUYfhgANkh2CDLT9dHMtGMRunr69P4+LiKxaK2t7eVy+W0ublpTlpXV5fROegky8EgIwhtD8XrqeSeOorMUPsqyQJ8FKMPbMkiAqCwR75ZhD/kXq6hstDBOZVKqVwu69GjR0ZTA3jBiIIachFsLSwsGM2vVquZYUAGzs7OPpVZoa50b2/Pgg5qKdvbz4eQ//qv/7rdB+grzlSxWLTgBaAB56ytrc2aTXR3d5sRI1NNfSU6wmc9cASWl5dVLBZ1eHiobDZrjo0HuXyWnkZzBOgvvfSSdRDFWYdSXSqVVCwWVSwWGwAKZIqsajQatTKMlpYWDQwMqF4/r0NfXFzUwsKCcrmcNjY2DBXnDJLhwMhGo1FjfxSLRWUyGTMio6OjunLliv3NdeXKFX3nO9/R/Py8sRjIUHV0dGh4eFi3bt3S8PCwyaSfqff666+bYzM5OWmGaG1tzZpS5HK5BsYDNG2fzfTPAXUQ44sB9O9va2tTb2+vrly5orGxMSWTSWvoRYYjGo1qY2PDzizsoJ6eHg0NDTU0ukHfJxIJTUxMmNzT3ZXsF8E5GRBJBmrF43Ht7e0ZYMi9Qv3s6urS7du31dvbq+9973u2xv7MEwi3trZqenraynMYGdfa2tpQftHZ2anbt29raGhIExMT6u/vNzmjSzWNcciKAn7hbEkXI71YB4BjdCl2j663iUTCgiZPSQNoIDDBIcS5HB8ft/Pb2dlpDAlsdEdHhzKZjCYmJj5li+r1unZ2dsyRXV1dNcAS8Ihu0uhTX5eKjvO2ulo9byAEwMT6AarQaIiu6WQ8CUDJWEoyXXp8fGwN7ggkfV1xyAjx7CPujfX0oCQOMmeaUWo4h/gnBHrsJcAPTj7Zrlqtpu3tbUWjUasbHRwc1OTkZEPTN+wVuoX7I6PMs21tbVk/lMHBQQOnUqmU2QjWAuAZWz8wMGC0Uuwk5wA6NcwV7hcfZG1tzRg1jDcjcKL7uw+EeZ8PhH1whIPs/0iyUitYc7yWZEFnZ6ex4dATfCesQJgegO2+M/Po6KiB7Owx6wvb0YPtyOnBwYEKhYK6urqsbIhAOJvNamNjw5qQxuNxraysqFAoWLIEH4/PYzoL5R3pdNr8V/pfMBsbn4RgCeDf3z8gMPoSn9cDDZxv/zd+JvoJ2UUmYrGYMcbwi1hjfGHkzv/xjc9g+KXTaQvyOeP9/f2mR7LZrPnrPvHiAyPPYsAnw28kSOzv77dJF6wJwJwP6tBZrBWgq2crepn1bE/iEW/XWMswSx7qV38G/DnAVhAA4wsQS/g+AvjiyBSvBxitVqsNzEY/9mtwcND2GPvpk5GcVU9R98+K3xCWVnkwnf3wMRH3y+XXDp2NHvbrw+99ptnLx7Ou5w6EfXRfr9eVy+W0tLSkw8NDQ9n8vMGHDx/qtddeUyQS0dOnT3Xz5k2jlWxsbCiROO8oWCqVTJlisHCuMJDDw8PmCEDVYiwAjmqpVLLMLdRnFoxaGtBpv7ghYhKLxSxgPT091ePHjzU6OqpUKqVCoWAoIEYtFotpb2/P6ESjo6NaWVlRZ2enJiYmVC6XlcvlNDs7q8HBQaO94iR2d3fr137t16z2mLbwOEAe0SgUCnr8+LEePnxoGQzqjefn5/V//s//aUCMJdm9ra2tNdQj9vb2mnGkGyTBE3Slzc1NQ/J+/dd/XScnJ3r69Kl9BoaeoBzn/+TkRBsbG5qYmNDo6Kh1in748KH++I//WA8ePLBZfj77wsGp1+sqFArW1IjgSZKWl5eNVtTS0qK1tTX7Lmql/f7dvXvXxmjMz88bnaWzs1M3b97UyMiIrl69ajWhh4eHNnaCZ8G5oMkVtUX+4CNTdNyDIoxz5esx9vb2NDMzY0g+Tciy2awFIowYINChvr1Wqymfz1uACSDFGfJoLPJJdpjMzebmpo3Y+Of//J9Lkg4PDzU0NGTGgE7fu7u7NsuaNcUgv/HGG6YIGQFWq53Pvn799dclyejQAAMrKyuGLtZqNb311lt666239N3vfrchK8e99/T0WBbz1q1bRp9cXl7Wt7/9bY2OjpqRJItMAyQaeSwuLmppaUkvv/yyrTl/Tk5OLDMLW0K6oOWUy2WtrKzohz/8oZ4+fWr3yDpRK8flO3jiSHV0dGhtbU0tLeedMX3G/O7du7pz544SiYR+4zd+Q+l0WvPz88rn89YVn7VC1sgqdXR06PT0vBMp3VmRxUePHtm5/OpXv2r0w9/+7d9Wb2+v7t+/r7/4i7+wYIRnlmTfhYNNRpcMBiARnf3T6bRu3LihqakpDQwMqLOzU9PT0xasQUd/4YUXDEiirs2PvMAxJWOZSCSsw2etVtOdO3dsnVdWVtTb26v19XWtrKxYycPo6KjRvUdHR7Wzs9NAc00kEjauo7e3Vzdu3LDRVpIa6ut8mU6lUtHo6KixYPr6+nTz5k39u3/376yr/+TkZAPt8smTJwa23rlzRycnJ3r06JFisZhRKClPCWmJPsuDXgRww9nHGSfrTv1mV1eX1tfXzbGhfhoKJvOZvX6t1Wr2WUdHR9YsS5JR8tfW1ixL1tPTo5GREasXbWtr087OjrEuYA/RiJFML7JDdpyAGP0LqIDO9SUFZBfpTurnSnonk4Db17j5HgLoas8K8RfvwT/g/wRuyAjsF/oReAdMumDKHRwcmANKlsXTEj01m3N47do1TU5O6urVqw3BL8/hwR9JGhoasn30XZuhfSIbUBpjsVjDtAr6JzDrGV0C8HJ6emoZsnq93vDdlAfQlTqXyxlQwd543Z76f6ODfEYYn8JnhdkL9oh/p1IpvfDCC+ru7lY+nzffq6+vz+bmYosJCqTGHgsEE3SyRW8zq3dyctKAMzLEvqbR00grlYpKpZJKpZIx2eiy7stVIpGIfud3fqdB1t544w21tLQon8+bHgAcQUZ6enpUr9cbdClBBEwXAiTAZZ6RZqX1et3o5ehCQG/WhjUBIObn7ANAKH1AYBZ45gx7FYvF1NfXZ36tzw4j4/68sefoHQI1dDHP3tnZadl+ZIb3cn98pg8esWEeBOc5kTXeSwIAkIhzwP0Q+HrGmc9yS2qwc/jG/t58HwCukBbtwQgfJLLHPij3ejL8GWvg2XiwE3luZI0zgv71o/PQcf5ePEDIc3H2vD1D5yAbvJefAVigX0hisK9ex/t99UAC9+TX6Xmv5w6EuXlJWl1d1fz8vBYXFxsctEqlok8++US5XE77+/v6i7/4C+OMgw6vr69bVhQHh+YC6+vrGh4eti61xWJR6+vrpohovkMTqX/1r/6V/uIv/sLq9cjsgoRiGBAsv2B+UcOf+XqLx48fK/X/Rjf19/fbhqLUQQl97SmHIuyOdu3aNWuk09vba9RqspGStLS0pHw+r/39fXV0dGhnZ8fWnnFUKysrDU13oH7mcjlduXJFxWJRS0tLVscACEAG0XPoeVYUC+iQV6wtLS0Nc40RNKghHlCQzg/j/Py8ZYp/8pOfaGZmRvPz83r69KnR6VBIR0dHNqqCrryxWMyypwADHEICJZp/9fT0WH16KpXSa6+9ppWVFS0tLentt9+2AA5aonQeeD958sTqPdPptKTzQAbqOUrDU6Y8HdTLDE4eSKmvrwINxZki20TAilIkEEeJYpS7u7uN2YBj5Q0KBiAWi1ndG+dKumiQQ2YIGvbZ2ZkFkXQj535pJlQoFLS3t2dONaM8QPhzuZwh6igzGiKQ8caoENBz3j755BPdvXtXDx8+tGeAErm9vW2lBb29vZLOHXIy2clkUqOjoxoYGNDAwIA++eQTtbS0WH3jwcGB1cEyEgkaP2sHwosjE4mc02bJTmxubiqfz2t9fV2FQsEUNJkQHDmvR8gGAVBgkKilpLcBzaKGh4ftPBUKBT169EiLi4taW1uzrsPUAyGD/f39Ojo6snmYpVJJbW1t2t3dVer/dZpfXl62DrlvvvmmPvjgA62vr+vrX/+6rl69qoODA21tbZkxIkNLwItsxeNxlUolOws8L042ASUjJiqVipWD4HR4lJZgwmehKT+B9gxAFo/HbQ65r4eHDo9jxfmORM77QHjaIzqDs02/hqGhIR0eHurevXva2dmxjBqBGPeK3mMGZaVSscB3a2tLP/7xjw2R397eVj6ft4AQwDMSiVhm6OrVq8rlclpcXNT8/LzJNoAbesU7HNgcXofzxvqdnZ03aCNApmSEpi5QK5kmQCdbZIVsLfqR4Ad9xF7g6J6cnDTM4mxtbdXw8LAB3cgI8uABHJwseiDEYjHTb+hCn5kF0ceukg3z1E+AKF/2QJaSYMizgzzY5W02fgL3ih7z2Ya2traGICedTtv+e8faZ3Cxg4CpPoPB/sJYQz+x1jw/cu5rkv1zEfTDviIzx+xT7pfyCg8KSLLgBp8APcA6ez/vsgtbRUkOGSrAUwLHeDxu/qAPCFgTAhz2hL9Z/4mJCZ2dndlc9v39fQPddnd3TXbJfAGas4Y+qIFWfXJyYnRxXyLhy0J8ptBfbW1tymazxgQ8OTmxkhn2qRnYEo2ej8YbGRmx5BDnke8Jqcp06wYIIfDm/IQBHjqNc0iwzc980Btm3f29+sAYfVCr1WwGdzx+MemBfaTLuNdpyJAvG0PHIgsATwCkPqiEaeFpvnyfL6PA5+E+kS+APu9vsMdc2CvOsE+W+aQZ55N7w79Db8HMY1RT6CeEl8/88n8faPL/s7MzS/Rxn9hb1p1A1FOFPa0a3YeNRJ580O8b8WFjWWOfbfYBKDbMxxWh7BOXASYgFx4gYA943hAk41k8gBzeE+97nuu5A2H/gfl83rIsXmEcHh7q8ePHhiz/+Mc/Vjab1Ysvvmjdkre3t22+rk+T7+3tqVgsWv0hr93a2jKqJMX5o6Oj1vTkvffe05MnT4yqRgDrmwFgTHyQhwPIwvpgju6dp6enWlpa0osvvqho9LwmmIAbweGPPwgElaBx1K5OTEwYkjo5OalsNmuOkCRD3R8/fmxNg3DiJVmWtFgsNnQwxUErFAqamprS/v6+crmcKSIvMNyfR2IJDGikgpHE+EH7JhBmzVBICCWCWq1WlcvllE6n1dbWptnZWd29e1cLCwsNTRRw0LgI3E5OTqz2jowOwS/rHY/Htb29bVm24+Njra6uqqWlRV/72tcse/7xxx9bzSAywPOvrKw0ZMgJdLe2tizg5kB62gkH3Q6RC5gBPqB/osj9yBvOinfucJo8lbivr089PT3m1LBXfgi5P/igxDjVOCRnZ2e2r2QnoSRzz1CsJFlTjnw+r83NTcuSQ1ekzhCqpa/9owlRqVTS2tqaFhcXTUm1t7fbfp6enuqTTz7RzMyMVldXzSn0dVhQsWmEsrW1pZGREWsIxIisvr4+yzp3dnYaI4SMNg47NFRPYUIGAbJ8E7Lj42MtLS1Z+QDOFXrGG1ivV8jYEqgCeBBorKysaHd3V5Ksg3ixWNTdu3f14MEDraysGIOjra3NQDFAhnQ6beN3BgcHGwJuziM0XoDI5eVlPX36VN/85jc1MjKizc1Ny+QRsHR1dZkcYuzb2tpUKBSMtuf/HhwcVCaTsRoudJ7XpR4x90Epr0XP4kwQeKG3cQyr1arW19dNhzG3nQAag44zybniPpAnScbA2NjY0C9/+Uvr5gqIiowkEgkD6qinB8xNpVLa2trST3/6U01OTqqlpUWlUkkPHz7U+vq65ubmGqYHXL9+XR0dHRocHNTa2poxWb70pS9JuhipA9DClAXkFJ3rdQVrenJy3jWbjAlsJTI+zKEm+Njb21OlUrESiWg0ak2G0B2cUcCjZDKpqakpA3Wq1WrDyDQauLW0tBhDBbaRd6DYfw8SsPfcH8/qM7M4vtgdZIrfs2fYFek844VzV6vVbOIC303w6i8v9ziRZCQYBzQxMWEZeHwFgmzvA/AcgFmAMMwX9r8nmKfGmh4q29vbBhT5DBP2iecmaKjVatYAyM8ChR7rS89wpLkA17Ez+AX8/VlXLHbRrd3fI9l8WDQtLS0GOBI4+PX3utTLAs8DgwPWFMEc5Xb4eQR93d3dFggDdKEXYDCiHwg2uC/2CVsEIMbr8eei0aj5vf4CoAsvdHdra6vNCsaH8MAXa+TtOxlp9nB3d9eaoIXlVJKsnMT7Z7zWy7+3jVxhcMy55lwQKOGPeGCDM8Z94mfjVyH3PiAnAKbkA5/Jj4lkeoQHKrD9PsnjEzrIow+4CNK9XkKOPbXZ/87HCp6pQQd6ZA6QlhKGUM+EMu+/I2RFNNsP3zsE2jc6Ev2BLkSe0EvcP7aEs0KAjBygP7kP2EP4jKwb6+Up2s0o1H59ubAf6GJ0uWfXsHc+kPeMEq+v+H5YkM97fa5AGKoujW0qlfMZtKTMyYh5ajMCgAJqb283OmF/f79lPNvb2/X1r3/d6n2glaVSKW1sbFiH03/4D/+hLWwikbDmWhsbG7ZoHj0A9UDo/YJKjYqXBaZJDQ4aCgOFihMIzcnXIu3s7BgKurq6qljsvCj/xRdftMANKiZKc21tTUtLS1pbW7POwdSVeAoUWSsCF0k2v5g9WltbMxop6+6zHFxkXVA0nq6K0kfQCbShZlFr293dbf+vVCrmGOFIzM/Pa3Z21rrAovQ4JATj1BhSp7a7u6tcLmeIYVdXl/27t7dX9XrdsmDcrySrY/3FL36hu3fv6smTJ+bU+YPoKS/e2EM3YQ9xQAiYRkZGzFn2Y2Woo8Bhp3Mzz4q84UACKqFEQLGpXaJLJXK4vb3dQPGk8269Xrd7lmRZMmTBo6G+vT5I297enr7//e+ro6ND+/v7euedd6xO++nTp5bdorO7dG7Au7u7dXJyol/96ldaWFgw6h9U7VgsZiBIf3+/1XsTMHN+qcUdHR01pghZDulcYVKr2NZ23nX7lVde0bVr1/TVr35VCwsL5mR/7Wtf0/z8vN555x1bNxrG4ECyH55+nUgkzJFAx+CMEhRJamiKxe9RvuVy2cAQAnfG+BwdHSmXyykSiVj39IWFBS0sLBig98Mf/lD37t3T/fv3LQtXKBR048YNc7Tb29uVTqc1NDSkubk5C2boe8CZwyF6+eWXtbq6ajRyMp44RDjUBP0ETOhAHHT0McHOP/gH/8DquLLZrBYXF7W6uqrV1VVztnd2drS9va1Y7LyJE530x8fHG+q3aTjGPlQqFW1sbOjtt9+2IBemhs+04RCSuaKh3enpqX784x9rcHDQsts4fPv7+1ZHOzExoV/96lf67ne/q//wH/6D0eIBBABlQJTJlq2vr6tcLpsDK8kaAp2entqcaZ8Vx3FYXFw0h1SS6btcLmc1utJ57SPB1vLystbX17W4uGgsGuiNkgxQQ7Z9CVBvb68ODw9VLpeNbphIJAygqlQq2tnZMd06Pz9v4IjPnLW0tBg9Dr0CrR/b1NfXp62tLZtj3tPTY2tG4MznbW1tGYWU87O+vt6QSaEJHnq2tfV8dujAwIAymYzdj3egcKj8RZ8B7AUOFqPqGIXjaXf4COw/eg9AmwxirXY+ohDmEPrEd8zl8plUggXog9hBWCcEd5FIxOzAF7/4RaX+3wiyfD5vYAlTHmq1mmXlfD04wBkOcEdHh/b29qwcAWcTfUYQ/+TJE6tp9fTP57mSyaSBcPv7+xZ4eFDMMx24F093xDb6TFhLS4uGhobU3t6u+/fvm49HtvX09NR0EZljMuJDQ0MaHBw0e++bI/nsK7YCOw14wX5jM9EXkozZ5Rtk+SD18PDQMu3+GhwcNFrx3t6eBgcH7TNpxkfigXp3KOhcS0tLxs4kueD92Hq9blRqn4FHH/pA2Jdn+EDMZ9yQ42g02vA+n9n39GfOGOAcNpLkFww29MHZ2ZnJNmA7e7W9vW2lYshDIpGwsVH4WSQr0GuS7DxGIpGGgH13d9dYGPgG0kWWlwvZ5eKMb2xsqFQq2R6hiwmyCVKh4PPdYabff094DnwgyGv4OeBBJBJpYPMABvju4bBS0HOecQSIgK9FYo++P1CpScjgl6E3Acd941oPiPs4IkzkSI00dh+oe4AR8II4xNOmOX9+DSORiHK5nCV3Put67kDY1yx52guIMugfqGJXV5fNhLx69apKpZK9j2Dg4OBAAwMDVhvmFYZHCj0Ctb+/bzUT9+7d09rami2s55d7wUHAvUIOs5hcHHKCH+k8wMrlchoeHm4IqjOZjI0pgFqzvb1txpWMmadpcBA++eQTW7disahSqdRQ98la+kw8gEF3d7dRNXFMJDVQXL3i6e7uNkfAI3wYJg5DR0eHIU2sKQKJs+IRexxFjKpHjlE+oOmsMxRf7hHDQ71hJBKx+lY+09cxHhwcNOwNz0Gn7lgsZp13UTh8NwEg8oaSQEH4bBMHmHXE4cPZ9MoEqiHKlXvD+fOOJf/22VyCZJ6N4ABaOM4tGV6ouXwHDTb8XMiurq5PzbdFkSAz2WxWf/mXf2kdMskygvDitC4sLNg+e5ofnVtBIa9evWrZNpSebwS2u7trgSsgVnt7u/b395XP580xpukWGamvfOUrSqfT+sUvfqGPP/5YMzMzevfdd23mNZ1Cy+WySqWSBQ3UqmO4uNcwW4m8evotsoUTibOLHGE4MplMA4WLz1pZWdG1a9dUKBS0tbVl56a1tVVvvvmmIpHz3gmbm5uWRYTpUK/XbV4omfovfvGL1m26VCoZYBCJRKx+m4AH444DVCgUrCv70tKSrl+/bhlc3xUdkIRmc/39/fYMOOj37t3TwsKCZcegxWezWXNK0U2RSMQA00gkosePHzdkAghMqtWqNjc3rWP3zs6O6c1isWj7RcOXeDxugR5nBiCK2mIcNhwcOvLHYjHNzMwYG8EDZbwOJ5SL76cr9unpqY3EAPwlI4UMo7v5uaeyeduDgUeXUkYDKIYzyf2xxp49whmoVqva3t7W4eGhnclKpaJcLmfyzJxfnEg69UNXrdUuaoWRd5o3MQedsh4yEjinicTFTG8cFgJwnG5AchxfAGT0owfrGDVDQNLV1WXrjj+ADeTfIeWXDDAAFz0aqJknu4Q+xaHGAeM1gEX+LEuNjRJ5Tk+/Zd2xxTB8AEhIFjBiDFmIx+NGV3/06JGGhoaUzWYb7KUHVUMaM5+LL+H1F/dWq9WsBAZd4teC58R5/qyrUqlobW1Nq6urDXWjBFAEhp7+7GvyvRPr/bRI5Lx2lz3w5whmDXsGiE9gQKPE5eVls7mefizJkhmSTPaxlb4RqO/BQYDm/TuSDnyGT6SEIM3R0ZE+/vhj0xecZ3wIdDJBOI1IGU+0u7urJ0+eaHt72yZ6+KwteovPxP5KsnWCWcF7wiA4zAbzc+7RByc+S8fr+R37i90nCYEP5WnRHihlT5AHzpCnWPssJDLN8wGEed+U+/BlBs2yjJ5ezu94Xsqt1tfXG8axEiOF7KeTkxPrQcE9ov+bsQX89Ty/D8Ej7oH1R9/DxCV+8X+QZX/+PMPKl1tgJ/Fz8aMJ+r2u4Qrp2fwM3e0TjugyZBTbyWu8vPHzsIQGu0cZ02ddf6NAGMQRGg4/xzmmY9+1a9c0NDSk/v5+u/nOzk6jEZycnKinp6fBgee7EFIyYGxmqVSyjroff/yxOe5QQaVGKhmBsA/+vCFAKSNInm5FxoEuyp5CyQEHgeKwlctlQ7AZSUEAA1p0enqqp0+f2v8xaqytP+Q8B4IDPZU181QQSdZYyNe6xGIxC5A52Dw/z40RBH3BCIBYg3B7Shefw73iEEhqQCgBJqCRcPBQvnw2QTL7R4bVU4Po7uhrH3AgOUgbGxuG/PrnY4+o3WB9cKxQjigfT5GJRqO2pyEdpKenp6EDJRRVSUZRwZFFdqCcsL4oYRzHw8NDm2NLUMd6e+dBOm8gB2oKst/W1mbdLZF7no+GOt3d3XrvvfeUzWY1NjZmdPGNjQ0NDQ2Z8VxdXW2qEzo6Omy0ysHBgY3bANCCjukdSk+7pbuv1Fg7ivOxv7+vg4MDTU9PK5vN6rvf/a6h32TdOQ+3b9+2jC4Ofa1W08bGhtVPAVjwO+TFG3uCXOSZP4AAyG5bW5uGhoYsa0vpx8nJic2FZnQOMkeQ+vrrr2tvb08rKyuanZ3VysqKda9E7nt7ey1zy3iNZvXnUMDRfQCRZOkjkYjW1tZMbpeXl3Xr1i3L8HkkFZkHUe/v79fVq1cVjUaNlfP48WM7SxhGnBaaXdBcCZnzdf40W8pkMlbq0NHRofn5edsnyghisVgD7Y5sHDOwt7a2LLCD/YBB5mxQV5ZIJJROpxWJRLSwsKBPPvlE+Xxera2tRomMxWLWodWDeug6ziYNxHwdJs4PRhzwDwYB9s3TBz3qTyBMcy4yy2S0fQaLdeUMoP84Z3Rf5/O9E+gzG4uLi7bndN/1uplnIJM+NDRko3VSqZSxj3x2gj0DOOnu7m4IongG9gbHBh2IU8O9ohNgK3Bffm/QodhDf9H0cW9vz3Q0gRqBODYHfwMnlfWkLwOvw8Z6IJNgyDvyXnZwMru7uxsmOPDMsKw4h9StHh8fa3Fx0fQm/gT2IGR24Vii48jweBYW8ru9va3NzU0tLi5aGUJnZ6f5M3ye1w1hPaV0ATbANqC5IvoIRxlgh3WBbYFz7YNhf5Gx8hRazgB7Eb7PZ9YikYjW19dNV7H27Bd6lXIfgB1K7AhCva3wzQpJUPgSD3/WcOrRU2dn5/XNCwsLJtPokJaWFit946z4DBjMqs3NTc3Pz1sW0vu1XgcCCtCcDj3Js7C+YXaSPWoWCJOQYh/wY9Fr7AXnDd3D8wBI+/Iun7BCnr2P5YMy5Jn9DEvYQj3nmXE+Q8lZIiHjs4veJ8OH4+ywB9638P1YAL783tNZ+296PSsgDjPG3t/jZ+gF1ptz5JmD6D+fRKMMCr8D1hsjcj0IGJZqNMt6S40UfGI9dCD6ljXjb/YE4BQdh21lv5DDcrms9fV15fP551rf5w6EUWyS9Pf+3t/T0tKSVlZWjDra2tqqiYkJXb9+XQMDAxocHDTBoYYX40ELeBq+DAwM6KWXXrJNnZ+fNyNLV1g2hyZdm5ub+sM//ENTVlCuPYIbjUYbDI7fAI+AgaJw8Hzm1GcGS6WSFhcXzdldXV3V9evX9bWvfU2rq6vK5XI2e9e3fN/f39fCwoKh9QQ5bCaKEOMJ7REHm07HkpROp/U7v/M7NroIWhVBAPUuJycnRhE9ODhQuVw2VMfXFniKAfTQsC4rFouZo43xwlBUKhdjnBirVavV9OjRIztMsVhMhUJBxWKxIXNGdpuRSAg0NGEOGU1/qLHCIIOut7S0aH193RxaHG0+BycUpBfkluHgk5OT5lgWi8UGGYGKyOid6elpa1LhkbZ6vW6ZT59V9EgtMuWDbjIIdiDj57WsOzs7BqpQRuBRM36eTqf1u7/7u5qcnNTu7q7+23/7b3ry5InR0nheL/vT09Pm1NPcjhE/8XhcAwMDWlhYMOWD8uOsEFwMDw8bOn1wcGC1vj09Perr61OhUND9+/c1PT1tAeba2pqKxaJl1Tx7wVNkOBfDw8Pq6+vT4eGh/vzP/9wyKWdnZ8YgePLkiT766CNzuLwewOjhUGDcONN+ZIV07pz7bJ4fHTA9Pa2pqSl94QtfUCqV0tWrV3Xz5s0GPbmzs6P3339f77//vmXLRkZGTF6ZO760tKSjo6OGOnXokYlEwpoq7ezsaHNzU//23/5bk9epqSnLEmxubmp6elp37tzRN77xDWswRxa/Xq/ru9/9rsne2dmZvvrVrxqYgqH269/e3q5vfetb6uzs1N7enr73ve9Z5pVMFaPp+vv7lU6ntbGxoRdffFGjo6OKx+MNGV70FI4AmSHfyZZzvr+/r5GREXPWvCH+whe+oKmpqU9RBP2F03h8fKx8Pq+5uTlzdkHm5+bmGhg46BdKXAgcqW88Pj7W3Nyc6bJqtWojPGCn7O/v69GjRzbrmCA5Ho8b6EAA48sUCHCq1fMGSTCMaDhJ8IQdQ3cj08gODoKvtcW59NRff+FsQn3ju2k4xh69+OKLevHFF/Xqq682OL00nmIUH80tYQFQb8xaAt6enp7amD56JWCHcGaQ3Y2NDW1sbOjp06ean5/X7du3dXx8rNdff92ofLBcAD65VlZWbDxNvX5Oky+Xy7p7965l0fb39zU2NqbW1lbLPHhnlvMPyAWw5wEy7t9fyDtnhmfD2WP9fad3wCv2CgCZey0UCmZXotGolRwgG1ATCbaOj89HjsFa2draslrwvb09FQoFlUolFQqFhnncPDOsAQAr6NkEzNJFYJHL5ezzC4WC2Xg/cgV9VygUzEbiUPtAmAtdFI/HrVaas0YDIp/0CO2sB4vxLWjiyBr70Vo0IQSgZQ1gEXBmkU0YKn7UF8EUNpN9xj/b3Nw0OTo5OdHS0pKKxWIDSHj//n0LzJBbggXv8PPZgOu83mfuWlpaTK+y5l4XhGsW/mE/fFDvM58EI2EgxeU/H//MTx4AUATATaVS6urqsikUnupMc0Y6VicSCWM6NWtI5i98XHwXdBcMD3Qne0PyqlarNZRXAUxD6ea1HqzwyQ/PIOjv77d79PqiWRa+2e989tdfYVbY763375FdgBs/9q27u9tmiLMXfX191vuhp6fH6NCSLLvsg+tEIqG9vT37N+vgGZF+Tfb29gxgh21D7EMc4JOPHpjwbAs+zydd8Dv+zqnRtVpN6XRaV69e1e///u/r/v37+uSTT7SysmIHjizG/v6+VlZW7NAg+NBJcDJB+nBsqtWqPv74Y929e9fa4uOA3blzR9evX9fY2JhmZ2e1urpqWUeczK6urgbEJhSoUNH6//vAD448dBXQfl+v29LSou3tbc3Ozurw8FCbm5vWJAW6XCaTUbVaNWocDjfILPfFIUaReEQNhwaq+Z07d/SVr3xF0WhUMzMz+uijj3R0dGQAAw4yQoGSwhk7PT01yojUWLhO3SBr4QM0n+HhkOM4U9fW09NjiDJNmWhQxEHw1CtQUhNGR0X2gZunjCFTYYBJkx1od9T2jI6O2l6SWcBI0em0t7fXQB0MrEd+yRT9m3/zb/T+++/rr//6r/Xf//t/V2trqylyKIzeMPvssqcMhaCMlz/fRY/14HxRe4eD1dLSYkEE9cFDQ0M6PT1VoVBQLBazsyhJmUxG165dM3Dg6OhIra2tDRQ77o+MEg5WWPvi5TMej2tsbKwhm7K4uKiNjQ3Nzc0pl8vZekDDi0ajevHFFxta/CMndANtaWnR7/3e7xkdWjqvySTY9dldPjMajTZQ8j2rAIc0zPZyz9yH35NqtWp1eteuXdOVK1f0xhtv2HfPz88rm83a+ert7bWRJ/fu3dPw8LDa29t1eHio3t5e9fX1NQT/YYfF7e1tc9aZIUzWFz3iG5+0trZqe3tbc3Nzevfdd82xXV5ets9lvBAZIjJANApD1ukc3dnZqZdfflmzs7P6zne+Y44/WXRAKQ+M4NxHo1Hrtk29KUarUqmYA12tns/zRIaGh4ctG3P16lVzMkdGRsxgv/jii59JFYNyTQOpYrFoNbwYXMZS0fl8bW3N9DRURB+ccTbK5bIF5ycnJ9a1fmBgQJVKRffv3zeHEWMej8cti42sQx8ul8t2rk5OTnT9+nVdv35dvb29FnhJsowHTjE6FKDTB2N8nnSRwfN6Umq0hegagvdIJKKJiQkNDw+rv79f169f1/T0tNlo5JV6aCjHgK1kXtEVgCySPpXNI3j3Z5F7Q4+SrYKtwvrCRpmamrIzDi1PkgXnlCZQ80YA6RvbDA4Oqru7285fSGPn+zmjXV1dDffudYUHj71Ty7N5JhUACAE1dol/05Crt7fXfo7D7XVUJBIxwIH1CzOx/G5nZ8e6muMoMiLPM/sAHNfX1y1QBeDs6emxWeYEs8gwpSn4ElCkE4mEgXr09/A02TAA9kCDD97q9fN6b5/xJNvuKbA+w+htLXo7PA8wlLLZrM2xxdYAPvn3ArwAkvIMrIm/OKucM9YHAH1/f18bGxvWuCsEhvxn+ws/ydOrvXxJF76TZwwgS82y781+5i9vc/168F6/rs2CNi58xFQqZb0ymJJBkOY/3yfieK9n632WXaDMCBAXO0l9MraVIN83WvL9gmAsMXYLvwt/KVwTrxe8b+hf4+XwsjVvtr5cPhAOP9f7GoD+yWRS2WzWGGowInyjV8/Y9HRxSRYbeBnwyUb0Gt8fi8UMRPCMpaOjI62urloiBWDUZ38BJtgX9DxBss/WE+sQK3iW8Wddzx0IRyLntZDMnhsbG9Pi4mKDoqJZ1N7enur1umUWPerpD6JHqyVpYWFBDx480IcffmhIMI4MswahR3uHGuQQA0V2AWXcLAvM/7n8oSIT4AWO+yWYBf2oVCqG8m1tbRkFB4MACr27u2ujC8jmeYWEQPnDwD0QjFy9elW3bt2yDOHy8rJKpZI5p9DMQ6oMShun1wfeBJd8H3tN7UYYjOJsYIgYaTU4OGi1mmR6aRLgqapeafnv8wG4R7CaKVrei8LyqBuGHwPCGBEOoncuBgYGGkbhMI/6+PjY6LEgkSMjI4rFYnrzzTf14Ycfamdnx1Axf4hZF9YWufeBcIjscYYoLQCRly7ayMdisU/R6gjQoZ9SF5NOp1WtVtXV1WW1xZzZO3fu6P3339fh4aGN5/HngfPoqZzUj0iNRg7KGCAYzy2dz3teW1uzzCAKkRmX1LiG4ABADhTuN998U//5P/9n/fKXv1QkEmnoyI3C9WvLvfmzxVnwdUTIIrLknXDe64Gel156SdPT09a1/uTkRPl8Xk+fPtXGxoYGBwcNDOjp6dH4+LiePn1qDXZKpZKh3dR5wfTwAfrp6anK5bIxELg8hRIAKZFIKJvN6uDgQGtra3rw4IHN8lxfXzc5weHleehIHV7oU+rHHzx4oI8++sgy+pwznHTo3OgXgmJAIcZusc6wXJB/HMzT01PL7rW0tNgIPbLunOvnudALh4eH2tjYsHnMMCy8U4gsUdJwcHBgTbZ8RgQjD40XgInPo8P9+vq6OWgg1GQf/LxbMsvIKvI7MjKikZERG7WEbiAQwchji/zcRWQ8zDRwJr2TFoKPgB/pdFqDg4O6fv26bt++rfHxcY2PjzddZxpjgeLXajULhrE/0WjURtuQzca5wbH0zxMGxD6Yqdfr1u25VqvptddeU29vryYnJxucMOm8PGhnZ8cYTui6SCRigIin19EgkEAeeSXQ4LXoFXQXa+6DB69DPLOsGSONvYWCiDzGYrGGmmgy9LC7WF/f7AldyPcQTCcSCXt9PB63Mq/19XXTQ3t7e2Y/zs7OGsbzYK9JWhAsMCNWuvANCPLCeknWZ3t7W8ViUXt7ewbo8h2suV9H5MIHEciMX3O/pmFm3v/e7xX7RClCd3e3stmsNWTLZDINvUOkC7uBLLDG6O9nBWPYLt8RHgCpVCopl8tZJteXMqBbKT/xIC6Xt53ej+RsoRfCc3VZFj68b/7269gsc/ysAJjvQf/BBhoYGDCWDyALMg2zhECIMweLwOvq57mi0XMm3u7ursnixsaGgcskvbC1fK4vaTw6OjKfFnYHGeEwtsCvQ2ZCXcwVJuzCvQ334bL3+Z/xOZ5SDv2/p6dHg4ODNlKts7PTWLXoauQQG805JaEgNZag8jsfCHuZBGiAiUJMtLy8bDqDqQUedOBvfHof6IYU9hCA8D7eZ13PHQh3dHRoc3NT7777ru7du6c//uM/1o9+9CPresni4MT09vZqe3tbo6Oj+oM/+ANTXmShEOSdnR1tbW2pWq3qhz/8oc2b3dnZUSJx3oWvWCzqrbfe0qNHj1QoFDQ/P6/V1VWjk0nnnZeHh4eVTqd148YN67xKRhmlEgqZd5jZVI9E+g0/PDy0US6RSMSMZjKZtKB0b29P2WxWlUpFCwsLRrGQZPRbAkNPU/BUF16PoYC28Pu///uanp6WJP31X/+13n//fc3NzRl9KR6Pm2Ld3d3V9evXDcECTIAqQ22RpwJyDwTLnk7E72u1mmUMJiYmNDU1ZYLf09NjlJO1tTVduXJFw8PD+vjjj62xABkC9s1TbnCmCW74PugbjBkAWaWG9ejoyJx1nC+M8SeffGKo3+npqVE9CNqpd5mZmTFZjMfjlj3NZrOamprSCy+8IEn6yU9+oj/6oz/S6OioBf0e7fLZVTI6OP04F9TQ4gj5JkdjY2NW15XL5Rpo/iChZLz9uCcCEObHtra22oza4eFhMxrMhK3VatbtGMXS3t6uqakp9fb26sGDB+bUAi55B+/s7Ezb29vWWdQzDQ4ODvSLX/xCOzs7FiiTRST4bWlpsbPkET9qOU9PT81YAojduXPHnDU6UbLXIMmgyShWziiOBO8HWeRZcEg9DfXg4EBXrlzRjRs3dP36dQv+fvnLX2poaMiCK7p3bm5u2jq0t7frypUrWl9f16NHj2zfqKGDooisHxwcaGZmRisrK8bwQL5o0uIvTzfE+Xny5EmDga5UzjsJDw0NWUO9vr4+mw8NcEOA1tnZqfHxcX3jG9/QvXv39OTJE0nS1NSUJDXMIqezbX9/6jV7kQAAlJ5JREFUv0ZHR/WFL3xBuVxOjx49skzr6el5J2V6LXhZg3abyWR048YNfeUrXzGQh4AUAIY9Qx9C3eLa2NjQ9va2yQJg6SeffKKf//zn1g3ZZ+H7+vo0MjKil19+We3t7ZqZmVG5XDZZx4nw3aTpig+d9+TkRMvLy+bkDg8P276SYa9WzxtYEYjVarWGxmysO6g8TBa+6+joyMo1qtWqlQP4c4j+JHvF5xKceuDSg4wnJycWTP7e7/2evvGNb+jmzZsNc+29TeRaXl7W3bt39fOf/9wyrug4ejig26GO0vimUChYJhmfIMyQ+JIhSUYv5//FYrEhm8PZjUQiKpfL1ufg9PTUwAI64O/u7tr4OJxdpjhIskwUeuzk5MTOKfKAbcFHCEFbbBj36+e+SzL/hxmy2WxW4+PjGhgYaKAewwQ4Pj7W1taWAe7IEAyFSuW8ezygEgEyZ4VsVSwWs9GXAHi+PpvzwXOQyQGMRSdXq1XrByGdN6QqlUqWafZBEUBJuVy22fAHBwdW4kRiwPtmoe+FLG9ubur4+FipVKrBGfbAms+8o9+xKwQF9XrdgIepqSmNjY1paGhIw8PDBtQB5EmNgD22IhqNWmkWrCoA7DBzywVgvbq6quXlZX3/+9+3Mr9yudwQKBI0A4pFIpFPNfnijPngF5kLgxD8UHxbfE9v15tlg33Qdtm/PVgQ6go+GyZJW9v5zOUbN25oeHhYV69eVU9Pz6dYgPV63XzZra0tm59MY0JKU5gicnJyYjFFs+vk5ETlcllra2taXl5WPp/X/fv3rWkeOplz7JkXPutOYA7bslkWOFw7r884a8+6/PkJQQoPOjQLjpEBbCWZ3q6uLqXTaUumjI6ONgTm0JzxWSVZTXkikbBZ3B64CwFVfC/OswcMaAi8s7NjscDe3p5yuVxDbxBPi8ZPw841Az3CuM2DXOzb81zPHQj39PRoa2tLi4uL+q//9b/qvffe09OnT9Xb29tw+MnS+boGgpVIJGJOETfpkbF8Pm+OhM8gU99VLBb14Ycfam1tzdAiFADjNDyCRIbLIxeXIV7+/wgRwW4kcl53evPmTeVyOXO2cDxoFHF2dqa+vj47VNyTz4Di4PHs0Wi0geoIzUeSoSh0/puYmJAkvfPOO/rlL3+phYUF7e3tGXWVpirJZFKxWMzqd3GEvNCAEPI7SVYPi5PGfp6cnI8x4bAwEqZYLOqTTz4xhUrgU6/Xra6JDBcosKcZSRd0bJxEb6z4fSqVUiaT0dDQUAP6SU0Nxh9FxniD09PzbtceUWZNAXD4Dpo6MC4GpJjsGM2jyAYPDw+bLK+srBiVDseBDA7ZO5ycZDJp4xw4Mx0dHXZuoJdDB6UrJDTTUqlkGUlPm+I8FYtFG0+GUkOh4ZQyhxYDgFOBswjdDSPqnT3povs6z0OjEAAZOrmTPfSoHOerpaXFqL84xZLMQezr67PROew/DcC8c0YWhT1saTmfccezplIpAyo8iu+zOWTFCIQBadrb25XNZtXe3m7dpw8ODrS0tKSbN28aG4LngnLL++fn521NE4mERkZG1NXVpWKxqKWlJZtrSqaR2jocUvQBgZfPDqJPWGMCSAwuJQI+AxePnzecOjg40M7OjiSZrHJuaSb02muvKZ1OK5PJ2PfTuApHlQZL8XhcS0tLFgCSSeGc4oQlk0mzCzAyCKQJLDmr0NiomY7H4zY2ztOr9vb2NDs7a/Ttvr4+zc3NaWVlRR988IE1/eM+z87OrGkPTpQ36KwJMk92AAeGZ/d0TYAMb6yho7JPUPsw6l6PoZN/+ctfamRkRKOjo7p69arm5uZ0dHQ+C9oj5FCYybbiNALuhGfU63z6C8Awev311/Xyyy/rm9/8pqamppo68Yw/W11d1ccff2xN3mhwhu7D5vkgDicYewpYjG3ysh462ugvAksCbOwyOsaX+mxubmpzc9OCPLL9c3Nz2tra0s7OjjWnY++wW/v7+6aDCG4olSFQQP44Y8g2cuQzQv7nvtcD5UN0MoehdHZ2ZmAOPgaMBTIorI13qD2r6+TkRF1dXQb8dHd3N3TInp2dtTFgHlzzNbHoAwJJfArA31jsvN746tWr6uzsVLlcNofXd3iFLcfzQGNkbQiEfbDrAzNPd0TmsSXQI/FvPNsPmUqn0wYsYadotInOHB8fN3kFOALk9cGRb5TImmMv2EuAB/bXn/9CoaC1tTW9//77evjwoWZnZ3Xv3r2mM2bZY0n2ebDF/IhKgDv0EvJG4OzPTgh2EMyja3lt+OeywCO8V8+6Yh0ITH0T3ba2NqXTaY2PjyuVShnIyOfgk+zu7hrA7xvo0V+H8wgAjr3n+QAtAF3pu7K6uqonT54on89rZmamoUeBL4lDtvxeECATBIdMNF7bLGtLLOLjpMuuyzLcl2Xk8Tm8XHNmKcmBAg2wLl30rPBBpKeEc7/4S4AZzApH3/mgGAAYdjDJqvX1dW1vb6tQKGhlZcUCYSZEwKBC52CvvGyFgbCPI5DNZn+e53ruQBhFUalU9N5772lhYUG7u7vKZrMNzi6BE4aOWjCUOI2GUJYg7KAF0FhROLVazTJOlUpF+XxexWLRnD4uMpFeefpsb4hYNVtEv8AoEOmik2w2m9Xq6mpDl9F6vW5GmeywH/8B9dTfQywWa1B+ZCH4N/dJNhZnmNe89dZbWlhYULFYtPeAqLDGHu1jLfm/Nx6ssafsoXhY31qtZp27ybRRg72/v2+KlXrxRCJhjWhQyjjQvpsmQu6No0ct+TkZYUaE8CygyvV6vcEokNGASkb2m4CJQxrWZnu6IQcbilp7e7vu3btnHamh+yOXrDfy4teeWmVoKWNjYxofHzdj39bWZgqeNSKDSoAAvXNvb8/qgn15AUacNefZcX5xisg6AZT4bonb29v2fgy8NwLhRZYaucEQbW1tNdSTcBFYsFacWbpbY8Ta2tqUyWSUzWYVj8ctY4OTguONrLe2tloGiPvF+W5pabE6OIy/p5+x5xhsWAlkraVz8GRpaUnJZFKlUskypWNjY5qcnLRgg8+E9QCAgdONvFIrRzCHQYJq7DMiACShUg+dG844+wJI5zPfAHpkstF7PitXqVRUKpXU3d2toaEhjY+PN2QekUtABmp9KFWJx+PWiA6jyeeTxQeEIwiIx+NGl4L+x71RgkA9He8ny1sul7W8vGxZn7a2Ni0tLRlryDcTgkniO1N64Aq95O2Dp12xD+3t7Q2gCrqKTqrYQK/rfGmBN+z8rFqtanZ21ua8Tk5Oqlgs2ppQ188+4jSSIWGvkX1sK06Mp3Myk/f27dt65ZVXdPv2bWUyGZMn5mqTbYXxcO/ePX344Ycmu4xQQd54bnQaLBTWXJLZd2TAZwLDjAfOKXuDg+vBkpApUSgULBimJhwZIfPGWDkozgDvZHkJhNGbnCnvnHEfnnKKHPEZ7G8icd4FvqurS4ODg/Z+9NHZ2XnjGN9wB5uaSCQ+VcMIwOWzgdg25AwfjAB1f3/fMrYkEXwgRCAcngV0UWtrq/r7++01+FqAZ3wH/UlgbNE8zmeaWFPWwAP03ilH93G2w0xPsyAEvZ9MJjUyMmIlFoDmAKf8GRgYMD+LJo6UuQHYVKtVA0jxGfBLq9Wq6SZsKeslnQfBc3NzyufzyuVy+uijj/To0SPNzc1pbW2tgdru/7C3gAWwKrDLsVisofwvTHJ4kA6b6XVbM9+YPbnsCvfJ/5s/BGDQbQEg0um0BcIEZSRefF8WWFyURHLuOecAQwcHB8b8kmQyzDnkXOMXb2xsGAgGQJ3P55uWCKFXwmwjusxnjj9r3fxaeZ87DOqe9/Lv8f9GZ/kOz11dXTbqkj/YVnQy7FQu31wKhoP/m2yv/z3P7xMy+KyUqcCC29zc1MrKitVZHxwcmA7zvX9Y72br5H//rHX/PNdzB8LUE5IpSqVShlYhFGQLPV3n5OREKysrWl1dNUNDIx8QOhqY+Nl43tFDYWLY6RrrlWsikbDMMzTUer3eMLLFI5HeGWbhPF3Eo6yZTEbDw8M6Oztv2JHL5QzxItCngYM3fqA03tDjBPBckUjE0N9o9GL2I91fpXPDUSgU9Kd/+qfq7u7Wz3/+c8sGtLW1GU2MxmMgcGNjYxb4QFWgaReGNZ/P273g6GPMOCB9fX0aHBy0bFg+n1c8fj42iGAL1NYbGfbQt9qPxWINDjyOEHuOAQB9hToMrZtGHWSjaNrzne98xw56NpvVzMyMjZ3wzVtwUlDE/tBwIHFM5ufndevWLcvEEpQNDg6aDIOI8XwEmdBohoeHNTIyojfffFODg4Pq6+vT8PCwNVpgHBjsAJxtDOvJyYl1U1xeXrZmSsw0xVHa29vThx9+qJs3byqRSCiXy2l1ddUc/fb2do2Ojuo3f/M39dOf/tQaCXmHn47hp6enJnsoWJ7VUzKhuvf29mpkZMSCW0Av3guoU6lUtLOzY8ACGT0o2gSp/N3R0aGRkRHroPzXf/3XJhNSY80WCH0kEtHW1padhYmJCZXLZRUKBXV2dpqz6A0g4zLq9bp1nOb3Gxsb9izb29uWISkUCtbsKxKJWH0lTnihUDAnAMcWKtDMzIyKxaLpSpwdHAkPUPksCEaezFulUjFEvK2tTWNjY9ra2jLEHLaEZ8e0tbVpeXlZ8/PzDfqdQIDnfu2116xD+ve+9z3LJGYyGQ0ODiqdTutXv/qVFhYWtLm5qWw2a2j5e++9p1dffVXj4+NGdwbx5bvQ4RsbG3r48KHm5uYMBGScmSQlk0mbQgB4VK/XDeBqbW3VyMiIZf3X1tYsq+4zQDhcBGnSuZNKsEwjOhxLqI4Y287OTnOC0PW+Y+zZ2ZkWFhZMf3kKJcGyN9o+I0xG2ffX+OpXv2oZ+O7ubt2/f98aD/n6Vt+1XzpntExMTFigTE05oNX09LReeOEF3bx503To7Oys/uRP/kQLCwvmMK6urqpUKl3acIRnQm8DQnnn0WcsfUadrry+AYpfH+8E+bKFSuV8FnhfX59u3LihdDqt3d1d6ypPnfzCwoJWV1c1OztrYEJY++sd5r29PbOl2C3sFDoFn+D09NT8GkoLCFqhrcfjcaMO09TrypUrSiaT2t3d1TvvvKOVlRUtLy9/ijbtL7LO/h58BhWGRyqVMnp9X1+fRkdHLfjM5XIG1JNo4IwD1Pj1CLP42GH2gzO3u7urjz/+WNJ5bxfo6MvLywZ2wAwM9ViYvfWygjMN8IQfBpgIk4j75XM4RzAqbt26ZdRlP3+c/UV/0sV6a2vL5B0Wm6dpElAkk0mbsIBNo0krLC6ekR4ST5480cLCgubn53Xv3j2j3Psxffgn7CvBuSR7BpgF6C8P6nkqtAeXkFPq4vm9TxT5rKVnb/nrWYGFZ1fBnstkMlaTSkd+7+/RtC0Wi1n2/fj42HxU9sKztWDO9fX1mY4GYPM+Za1W0/b2toEZNM7b2dlRPp/XwsJCA3uBZwc09AAb68jPYaF6n9Ffof7zrB/f2fqy9WwGMDR7T5jMw9fyfWR8OUlYTwubxdPmsausB3sGkAyYhJ9CXyJ0RCwWa6BDr66uqlgsanNz0xiknDUP5n3WmoTr2yzjHq47AN/zBsTPHQijlDySRBbKowIEnQTFW1tbun//vlE7I5GIOdnxeFz9/f22IQMDA+aQkSWS1IBwYQRA5j0aj1I5OjoyAaCLKBuNk0Swws9BQcMMiSSjst6/f98UJ+iiz2KFG+oFmRoI/3MMqKdBksnzlGje9/3vf1/9/f1mmKAr+ABzeXnZspBk6XA6cbyZe0tHWoKLnp4eQ+G8gkgkElpfXzdaIw4D1FSUcTKZtOwEzweC5z8LBwjnQpIZe54LKhRy5EEBFGgul7M51RggKJWM8vAdM3FsUXS+ls4jzDhgpVJJb7/9tmUQvOyRqQJpQ0HSUbZWq1mrf1Dcvr4+dXR0qFwua3Z2VuVyWfv7+zY6y9fAEHyDmrFPGF0aUJ2dnXfcTSaTunr1qtH0Z2ZmNDo6qsHBQfX29qqjo0PZbLaBntba2trQ6t83JCJ4gnYDUgeQAEsBCh7PnEql9PLLL5sBot6TvaOpHUCaB0rYn3w+bxmter2uwcFBDQ4Oamtry6g9NCjCePb29lotII52W1ub8vm83dvGxoYFTB6BRIeEGSYAOPQL+93R0aGlpSV1dXUZLTAajSqTyWh0dFSnp6cqFovWMRrWSywWU6lU0s9+9jNbRz/iyTMJvFMSZsq848LM9uHhYdOfZPsJMggaQdDJQofBGfr4i1/8olZWVpRIJPSFL3zBWDg0s3j69Kn29vY0NzdnwB6dl9EzAA+Dg4M2Ymtzc7OB7vT06VPrvrmzs2NGcWNjowH4pHafbGl7e7vV2XPfsCc2Nzc1Ojqq3t5eCwiop2Q9yeREIhHLbLW1tdlsYgI7D8o0ywIANpRKJcuMAzp6qiLPHGYDvL6JRqN68803FY/Htby8rIGBAb322mtKpVK6e/eufb+nzvf19Wlra0uRSES9vb3a2dmxwO7VV181kGR9fd3OLxn6mZkZ/eQnP7EgkUAcWfYUOemiWWRoh73exEnv7++32k9f5xWJRAxM4H68vfTf4x08sp3YKMaRsZ6MNbx//77effddra6u2hgf/JbQvnAdHx9bs6S1tTVz9CKRi7FF6GN0Cr4MTiLZqDfffFP9/f3q7e3V1NRUU2D1ww8/1Pz8vIGInEGCYb8W6CifOec+cETT6bSGhoaM1o5+zWQy9nzYcwIwQAsCSb4TneT9EZgbdEbHzu7u7mppacmC3lwu11C37cEPdFhoY1kf9sjvTRgcc169vxayyOLxuEZHRzUxMaFsNtuw9gDmgJ3YVerGYQ/AkiJL6RM8sIvIZiE3+HbojdPT86kNGxsbyuVyevLkiZaXl7WysmJlcHQx9kkaZMEDXQBB2B3f8yWfzzfUePvyJTL7npHpg7gws+nf74Nqv1dcoS3y2WvfrRz5CoFn4gb8T5IvTEdAdngOAmHGJvnGioyF8mVulUrFAl+a+lGitLm5qZ2dnQbwgGfyP/NlGV4eLwuCw4A1TK7wLD44DgO1MNANf9ZMP/A3ZUWAp9jicA8B0X3SzZfxoAdisZiNSyLpQUzhAQ6eD7mHKbS9va3l5WVrzLe+vm5lMZ69FD6bl6lmv/dXyGZo9u+/80DYZ2f9BWKMUfIBVDQatdFCNGwgUOVBfbv4zs5Oowf6h+UikGIBfL2fD859ba5vygSi6w0Lnw+y62lBLCJdBO/fv99AGfOov79nLq+gQfk8/QXDQ0AI+ujr+vxhnZ2dNTpaSA1CiZG5r9VqVl+HQkQACYJ5D+uRTCaVTqcVjUYt6GVtyLZjiL3zTp0awTh7xVp62rCf0erX32f5pEZFTsADOLC9va3j42M9ffrUqDQo9tPTU21tbRnC6y8MqldAPqPIawgwK5WKzQ+lwZZ3FDFmnv7raTy+XoP9gnqay+UsECZgZt4jgcPGxoa9BmcZ6qnP3nZ3dyuVSimbzWppacm+n1EQvvGGR2U9+s7nh3+8vLKv/kyenJyYcqOLtq/b8fQmZCWdTiudTpvD5ZucSOeOLaACHWm5OBMewCCYJQjz3XQ9qs99orN85jcExdAxBFjMmmW9kMPV1VULaE9PT42xAusAgw1Lg0Yv/vvQE35t2Wt/JryRQLcx8iCZTJqcogc8JQ6UGP1CoOezcHwmna3JgsD8wAnEsG1vb5vDgy5Bz3Au0ceAJqz1ycmJNezC6eRiRBAlN5zFer2udDptVHl0BMFxrVZTqVSyMRwjIyPm6ELdZU9xeskKYH/IoPoMJwEfugPdhpywLuxbGPCyX/7v0KGp1WrWxZ7vBjDyIyU8GO0bw/jAoFKpqKWlxQCSRCLRQJMvl8va2dnR/fv3rbzmWRc6LAROeEYCwmQyaewhMsYg/8gb5TVeD/t19rYCtg1rBuAB6AX99ujoSPPz88YsYPwaelNSA3jsZR49Q6aVM4d+Yb+5R+y+12kENgMDAxodHbUGizjXhULBGiTRhBDfwdMlWc/QPvEz5BA9Qc0oXa/JZAOYRqNRm0XvgXjvf4SBJPvMsycSCcs2Y3NIXDADNBKJWP01GSbOs9frYUDlAekwAPavDWUu9AnZUxIA7CX2kXtBjxQKBQu88E0JCPC9YGb5PeDzOIOFQkFDQ0Omo7we5LMJiH0pQRjEe9uLj+J1qe9RAChOV3RfghgG1nwHZ8TXX3pmon/G0C41C4KbgRnsC76QT4hEo9EGX5YkA34OoJD3OX12HH/e+9ycU3pD8N3eRyTwgpKL3iP+8L6+94M8IB7KbrPAKpTTcH34f7OkS7Nz4T/jeX6GbKJv8YV8p2VeR/BL8EpigPtDB6KXSHpxHhKJhIH76A/sytnZmemBQqGgfD6vzc1N6+lEtthnssPnarbOzQJev9ahfnjWel12PXcg7C9QRpQNCpggKaQVkfWp1+vWqIONoJGPp7mAArEgZJ2i0fMxQpubmw3OO04n3eXIToJK0/iCzKWv4eGwQY/E2PiLroKPHj1SpXI+AoTuw5KsfhdH0h8o70R4R8LTnlgHr4xxhjx6cnR0ZM2TyAzgdLIPOCO9vb2WxalUKoaQ1mo1m+1bq9XMcQZxGxkZ0d7enmXXMGoEm9Q/hkEvmQf2DiohzVMQShxX1sEHI77ui0MJ8IECIfjAmZiZmVGtVms4nNSEMLsVZ8XT/PzhweCAaHrjAB350aNH1rSGzASfSbCBjFI+AJ3r7OzMKOiHh4d69OiRGVyoVb29vRY8rK2taWVlxbp/7+/vW0fn/v5+e75oNKrbt29rdnZWqVRKr7/+upLJpG7duqVvfOMbmp6e1vHxsWZnZ3VwcGDBAXLLOBIcILKIOJ3IsAeu+F0sFrORSTikx8fHevTokQ4PDzU6Omq0zmg0ap1RfVa1WCxqdXVVkhrWb3d3VysrK8rn83r//ff11ltv6cMPPzQHr6WlpcEJaGlpsXKFTCaj/f19FQoF1Wo1vfjiiw01g5xxSSYv6KnQkFNGQPaN7qibm5uamprS9va2vvOd7+gP/uAPJMkQ6OPjY8uKcZ/T09MaHx83ejDzSgnguJB9DyR5Q83+UAc3MDBga1wqldTa2mosAHoppFIpC06ghYWBG99VKBT01ltv6V/8i3+hUqmkH/zgBzYGi47XjDugZr9eryuTyZiTBh2vWCxqcXHRapG8k0FdIWg+FPKwbKRSqVjZTKlU0vT0dEMdYCqV0vT0tNGxkO2WlhZlMhm711KppAcPHhhAsLS0pFKpZLR8nFOAUy8L2Dd0MmBBIpFQuVzW+vq6Tk9PlUqlbJ8AXJElHxx7pz8SiVh9OI71K6+8oj/5kz9RPp/X2tqa0ZRhCAF0Hh4eamhoSMfHx9rY2DAdWK1WdffuXRUKBWMYwTaan5/X+vq6isWi2Suem3v02XDPxvJsKfY9EjlnZk1OTmp8fNxmQPO9ZMvJOhDM7u3tKRaLNXSz9UA368aawviC2bKxsWH0u+3tbT1+/Fj37t3TwsKC2UvfqDLMdvE8nH8fDFNqAiumo6PDnHTWg5q5s7MzDQ4OKpvN2mi43d1dzczMGN1+f3/fmotubW2ZrQvpex74CjOF+A74KqlUSq+++qoymYx13fVsEP8z2EY0z+ECfMbO4gSzPvF4XC+88ILR0hOJhPU/wZbCBiEoJrvrdYt/JvYagMzX4frgMAwWcLoBR8jcerZRW1ubtra2VKudT0RIJpPmQx0fH2tzc1NLS0sNmX4ypb77dBiY+mcgeOA5Ozo6NDg4aFRpmDdbW1taXV21pohk4Uga8KcZEILcUk6zuLhouq5er38qEPYBbij3vqYcXwX9C7jG5YHlMIAKA5EQnEX2YLL5Rq2SrDMwgRfUc7KwHpDxSSKSIMQN2EjsiWfg0MyScYGlUsn8RfQF7/PJK/aUy+u5ywCB8GoWzDUDcMLseLPP8YF0s7/D1xFjAXbs7u42gC0+A03Q6sELrw+RewJeXxPf09OjdDpt/ROkiwQC0w2WlpYsEF5cXFSxWFSxWGzosM13haBfuFaXyd5ll/cbLsuoX3Z9rkCYm+ZQI6goaYIjbogADUPDZ+BM+856kkyZgq7xuWRiWltbNTo6apkGHBOCwNHRURvHkc/ntbq6qkePHll30Gq1at2PcXIwEhyyer1uNDoyLGQY+vr6Gp6PQ4zRYvPI/JIdI+CVLpAuP2KiUCjo9PTUHEnWyCsJ6Xx8CbUA3AcODE4LFKhIJGL3Xa2e17uVSiXL9EBz4DtjsZhWV1fV3d1tCgU0EiRIUsMhZn9wfH3NEoGedy65UqmUWlpaLIBCEWF8CXSk82w8tQrShUMjXWSXcQS9owNYgSHkGTggHhDp7u62on1+f3h4aNRq6N0oGu6b++jr6zN5Ojs7s9ofTx8ku7y3t2fUX+ShUCjYeQJ0KJVK9qwYk1wup/X1dbW0tNjMve3tbaNu0fEX5wJnl8ZmrBl0SAIn5CaRSJjB9HvmlSbrGYud1/pR44YTdXp6Pmsvn8+bc3lwcKDNzU3dv3/fGvz09/dbZ+KJiQnrrHx2dmZGslwu62c/+5l++tOfam5uzhxHjB1ZQ1+WUK1WG+rtWcfOzs4GQ8wVKlxPCQ8RfkAg6vChDD1+/FgbGxvW1do71owu2N/fVy6Xs86JyKSkBtnmLJNp9eeMsxFS5bysUL+LswYQhSFC3nt6emzf0HtQUPP5vB4/fqzl5WX95V/+ZQMdD9AHfc5aAwaSxaQsZm9vz1gJ1IjTwCgajVq2Cp3JM4fOYjKZ1K/92q8pk8k0ZIW7urrMIYrHz+ekzs/PW0ZmY2NDa2trunfvngWs2DDWCMASXU9jKtaHLA0BOM/rs8Oh0+GNPXvobSiXd7rpgF2tVvVHf/RHdkY/+OADq9NCNryMo6NhYXDPjJWimVutVrO53tgEZA/968+RZ1Bhz3hWwDMo6JOTkxocHLQRUszCxgGtVCrq6+uz8+fLZwhAsY0eGOK7UqmUKpXz8WM/+tGPdO3aNdP9NGLZ2Ngw38TTq8OsDvaafeG8JpPJhnEe7COgPE45negrlUpDvTnZxUQioc3NTV27dk29vb3a3d3V7Oys1tbWGu7HgyXImHd6Q4cOwBmHlGkaNMAaHh62ppa7u7vmY2HfKStBBn0TOQ/W+7X32TGyTL5519HRkdbX183mhJldb0N88oKAG5DG2xje30xH+58BMgNg9/b2an19XVtbW+rp6dH169eNrpnP540OTTDEPbLuPHuzLDav4dzUajUbi9bV1WV/JFmAT/8LeieEjBKv68LgiHWv1WpWw07Drnr9orsyvlN4ZvhcnxX3uibM3Ptgyeumy4KRZlm6arXa0IjRU68Bkfz/va70exrKgKfsx+NxO3d+n/g81gV67s7OjjVtYg+wYb5u1idgPDARrkUzRkz472ZZZPw736nZg4uhrH+ey8uoj0WaBZP+td5u+d+x3thDRg0CXJLIouwAoHpnZ8eAn83NTa2vrzfEF+yvP3shCBI+U7gmzbK/4XoRuH+e67kDYb+gHvELlZN0cahw9jE20qcDKZ9p8sXr3uj6bPD169e1u7trnf0wyMx9nZqa0pe//GV99NFHevz4sQWFICYof4LxsEMvitWjIjxbNptVuVxuoPJ5JRluEjU7ZKc9ast8OIIqlLDvVBluMEqNgMEH4V6oCdagS+IwEQjg0EGzJehmLiE0FRQZmQOeye8hiomGEtTNlMtlc049pTQajdqoAc8iwDj6LDABHwwCTxkPazk95RMHG8oqshoeNJwckFZvkKvV89oaAh6areDEsRccbowaIA3ZUJ4lFotpbW3NUEqUbmtrq3VrRgnxLD4gi0ajFnR2d3drcHDQmp4hs/l83jJdZGMITiORiGWoydCwBpxp2Ayh7IUZLc+Y8PvqaW6+xpCAjJ/RUv+VV15RR0eHMpmMZYy8kT47O9PHH3+sxcVFlctlc+LQK6whZwIg7uzszOQbeaKRm6clc3k6GHvn5QUEvV4/bxbDzFFkIJfLaWtrS7FYzJqf0Tgnm81qYGCgYWzRZYYQHYID67ua4wQAhOEY0P0VJ4BACKYIDZY80EHjEerM2Uev32dnZ7W4uKjl5WXbQyjL3Af7RUDCesOuIRjhnjo7O7W7u9sAprAGPtOGIWM9cAKvXr3aMKaEe2H/fJZnZ2dHxWJRuVxOuVxOjx8/tkC+VqsZEAnFVlIDsOKZKv452Q9fX44OCp15zhYgHI5KM8ONjaL79oMHD3RycmL6GfYALCfknu/yYCGfhb4EmGOffKDuzzH3yr55x9hnUMnMAmhNTU1pZGREvb296u7u1urqqoEd1GMid2QkfLdbZBtnCUfV3wcXJSMEI7u7u9Z93euQyxzK0DH0JTn1et3qpTn3vM6DYr5hFTaM2k9kiZp4AD1fpuPP3GdlL0KfwtfOo1PPzs7U1dVletnXASaTSXsmbLmXc0+D9EEA90JGkXOD/SOwQMf4oKpZcOD3kb3FTjRzWps5wZf5mp5ijqzVaudlEmSCFxcXzcaG59p/Zhj08R1ebvhu+g/4elvkF0YEfRVYP//ZgOQEZ+F55JnxaQgA/fu8TuGzeT6vE5E1LpiGvNc/47OuMGgK95qEAiy70N6Gn+91Fn/7M+H9K2QGPw2d64EAABrYdXSJp+O6XxOfVGFNsf+hTFy2Dp91+dehK3wpgP+ey94f+q2XvY599Gv3WXvaLBD3LBJA81Qq1WBXWL9SqaR8Pm+gbS6Xs54ejLr0yQX2zMvbs87/Zf8On+15AuRnXZ8rI8wXe+cLBUjA5WtsOJBsjq//8RlNAlCMYkdHh9VTYLQHBgZ09epV/ct/+S8twLt7967VQf32b/+2vv3tb2tgYECS9Fu/9Vuam5vTo0eP9OMf/9i66JZKJdscjAFNt1hMHFCyW2tra9b5N5/PW+deX3+LA4fxpl7Fj/5hpAjZHhwdDme9Xm9obuEFWpKhXSAz/A6kGComr+/s7LTufQsLC0qn09ZsSZI1IGHdydgcHByoWCza/rFOBBpkFCKRiGW7KpWKrly5Yp0a6/V6Q6YU5JdZZhw2nHYCPBpNML7l9u3bNsPXD0FnHA7U87BJDcHL4eFhg/NMnTNU8Ewmo4mJiYZOgqzFwcGBxsfHVSqVtLS0pEwmY05cSKfEMcvlcuasTE9PW3DS19en999/X/l83oIBgjTvoBPAASJw5vg3dDBkaGtry+az/u///b+N/gO9nXWuVqsmw9DWJZkDBZjhz6x3isIsF+AAZ71arWp9fd3YDmQrtre31dvba1nU9fV1c1prtYumaMzgrVbPZyHjMP/pn/6pOW/UOmIcoBXv7u5qeHjY0HOaQ01OThqDJBKJqFgsmvN3dtY4tP0yVBZ5oukXo7Du3bun09PzTsoPHz604BXaJ816+vv7NTQ0pO3tbaVSKZ2enhqi6vWGByOgePuxTL7rJkEvwCEZCDLgdI/0zWvICI2OjioSiainp8fAEIwadOeRkRH94Ac/sNpaDxCyJr4nAnqawLqnp0e3b9+2emhkdXBw0JwWzgC6Dl3E8/KZZLAHBgYaRjkR6ONkoRNo2kOfABp7ERTg2HtUG4f29PTU6PstLS1WQhKJRCyjjdOLzsROcD6QZ54HnQS9FH0fOnycc+qqCoWC2ZNsNmsyPDQ0pFgsZrPQqQ8fHh7W8vKygTXoqNbWVmsCRbd27od7YU/95bMzHsClXnRoaEhjY2NKp9MaHR01WnalUtHs7KzJHjJar9eNzRKPxxtmXvvmOqwdDilsB/TKycl5Z3CcMfRWe3u76QPOCTo2zIx4h5FAMRI5Z+b4TAnN/o6Ojozl5Dv9p9Npk71CoaBsNmu1ei+99JLy+bzNWga8g9YeOmneoUM3hFkd6aJ3xsnJiT766CPzV15//XWjztPckICMmcokA1jbjo4OA5Tw43yvDYJe6i2pO67VahZg4AA3y0KF9sM/KxlhfMkQmAnBQX9GvENNhjYWixlYij16+PBhQzMmbFX4Hf77vc8ZyozfH+6HBAPfgxwzCQVWmQ9aufx6s28AXMigz6ZSX4mfDVPBrw335OXcJ2p4TvQdQasPhv3nhcB4s7+970yTW74vlAVvM9BvvgQlPBPYftij9JqgHAgwhvMCW6xYLGptbc2ouewBz4uNYU/JtIeg1OcJpsLn9p+Fn8i5ZA38e7nC4LvZmodnxScUL0ukeTn2ILT/HJ+8ozcSvhvlQYVCwfzMUqlkjbC2tra0srLSMNarWYAegjPPAzSEAe/z/P6yILvZ9bkCYb7EZ2D8BoCaShfZGo9A+uCZyztjBJUEqRwQxhOB/A4PD+vGjRt68uSJUZNWVlb0/e9/36i5a2trJvzSOX01mUxqZmbGAjcMNDVRLCBBPBRKhntTgzYwMKCenh5r5w7ay6biMEEDlc4Fj6Yqu7u7Wltbs//7DFgkEjHUj3Vlk32jBrKsrCFBdbVaVSaTsSAAChXZURBq5hZ6mhtUOG9cUBieXsPz8JqpqSndvn1btVrNlP/Vq1fNyaR2EqNB7YGfDQy9mi5/kqyZwosvvmgUXmrGfOaaOjP2wDtvHk30RqKrq8tqHVZXVxWNRtXT02Mjiba3t3V0dKSFhQVrPENXY6i2GKhyuayuri4lk0m98cYbtt6VSkWDg4OKRqPWQRInivUMEVO/3x459M9C51Icek/XBlxinfiOg4MDra6u6t1339Xv//7va3BwUJFIxGhM0sVIFK+kvPFEvr0C9TqBLGgkct5VOxo972rs63JY80qlouXlZQOdMBRkv1BkfoSNd1B8LRtN9vgOArVEIqFvfvObVkt4fHxs5xgD7pW0zzL4/YCaCVDzk5/8xIAXkGbfxdI7asy7HRwctA6jyWTSnEwyZTwv4JkHzNjz4+PjhnnZ3J9visG9M64Oh5ZA1jdX8RlkjHS5XNYnn3zS0EmS7rapVMpq80N5IMAmcBsbG1NXV5dKpZI5pt4w4fAhQ56m6+WNnx0dHemnP/2p6TMCay4yU8yM3tnZUS6XM1ASB6Rer9s+eaaSJNPlgFHd3d0G3JXLZStt8J3dsR3YD2SLIBuwjn3xLCofPNRq5120Ozs7NTAwYOcB0AY7F4lEzLFHzk9PT7WysmJ0exrIsMd8hgfg6E3Bxf2yPv4e2TeYV+l0WuPj4wZ8dnd3a3h42Ob1Qv2Huo/dIivJ53uaJHbNszGwQ9D/Aa0HBgZMrsiQA+TgIKMzASN83w0P6tGVnPPIZ/F93h7ixwCW8lr2G4ccdg77QNkAfUm8Q4rP4S+/5p4pAeh7enpqWa6+vj61t7cb4NXa2qpUKqWFhQWjhsKgY3/5voGBAevZwB4RcOE3cZ6gi2OXcIZhdnCvPE8YFIfPyD57Oipnw//fMxXCvUN/47/EYjHr3XFyct7JGt3oAQh/7vw9h5kq9I8PPGAstbe3W98V/MRIJGK9CmZmZszeo4e9Pfc+Fb4v9+EDS29vwwCcTFuoU3hNGNxy/z5pcxn40OzvMKvsdViz4DcE+/zFGfUgtPd1AG4Bn6H8Q8WvVs+nIfT09Jg/sb+/b82aZmZmDIjzM6/DDDT/9gGTXzMf1IZBW7O1888cPjcljZ5p4teK6/ME3/56VqDs/x2eJ3QMYCMBcH9/v5VQItfoHhIONMXy000uC3Dr9brFVT6OeJYufNYVAg6XPfPzXJ87EOZhyCT6L/ToEUrE0ziaKUf+8Hu/QB4pwqHb399XW1ubUWZxSAuFghnxSOSiMVI0GrWDEIlELFuJUxgi4V4h+uAcY+yDZ+jDvr6UgNcbMCgyZG7K5bLRCaC2sl5QcaPRqGVKvSLy1HE+n6A+EjnP0A4ODurGjRvq7u42Q0cnX15LsOENGM6kp/l4gIB9xMHACenr61N/f781C/Hosj8gHACya9Av2APmHIKIHh4eamVlxaimOB5eUYX/Rx59IzKMjpcv5ATgoqury2o62W+cm56eHvX29jYgiB4owFHq7e3VnTt3jLJWKpUseNrc3LTMQJhZDe/dy6EPAv1rWM/Ozs6GOiHkg73zDi7BMM07CBB8wOENvj/D/uJ36AGCT2SF4NvTd0ETvR7Z2dlpGPgOfZTn4Dyzh34N/D3F4xddDTGgyBM0cQJIwB6cvhCFDg05jhf3B+AAGs+688fT5cPf+RpfznNoMDjXPD+BM5lG6EY+CPLUO54Jp5+L39GkyOtc9g69CVgjXXTpRh97GfWgBKgxfRXYb4J9AhV0JjIePnczGaxWq9rf39eTJ0+sDwSjegBfmX1OjRqzU/19IluerYRzhGzt7u42jEVjXvfKysqn5ARZ91lH9LV3oLEFIeDC83N+KJnw81t9cMB+AP4RWGEX+SzOhD8vyD3BX0j/a+bgeocpEjkHaHt7e43un81mreM9dele/4Sfib7j38gf+gJ7xr17nwEZjsViNrLK74MPkrzs+OdsFpR5OfXAQK120UgSME66oM9LatC5yDf7vL29bfvFM/tzHTqrzS5ex7r4rLnXKYlEwhxQ9MDGxoZl5cM6XNaK3hft7e0NdGEPrPH/09OLkUI4w54NGN7zs9acffFZ+xBcDcHX8OfNMv7SRZ8FbLivcQ8DHn+PXpb4WbMAlH2Lx+Pq6uqyjvgAU2TJ1tbWDEz3GUgPnISZWq8P/Jr5/4fPGgarzQI2/3vOV2jbw2DisvdfFrSFe+z3utnnNftsXu+puYBfAOaAtfV63WjQJIGKxaJRcgGiACGa3X+oW0I/xz/X5wmsQt3nz7CXgcvW6vNel61teHaavc7LGMzKrq4uA4ClC38eRiqM2P39fRWLRZsDTbwS+mehbfR2yYMrnxcAaBZA/03X8LkDYa9MQE6li0MaOqq+NnRjY0O1Ws2yJhjEZkJBgEWw6euMa7Wa1tbWdHp6qvb2dhsNcXp6qtnZWWsmtbe3Z801VldXrW7k9PRUQ0ND6uvrUzQatZpYjJ2nE3F4yByenZ1ZAxmMAErOK5nW1taGhiAPHjwwuqOnAUGl9cELh9ujkt4I+wwihqy7u9sy0NVqVdPT0/rSl76kX//1X9ejR48sIMWxam1tbZg3CUWNTD33Ar2SzJpvzEDTjVQqZYcGVBmQAooE2VqCQ7pOkqGGilir1bS1taWDgwO1traqvb1dx8fHKhaL+tWvftUwfxbasu/wzPqh0DKZjHp7e9XS0mLdr/v7+81w+swU1MLe3l719/drcXHRMg6sczKZVKFQsPUhUEBexsfH9dJLL+mf/tN/qp/85CdaWlpSKpVSMpnUxsaG7YWnlEqf7nQHwMT5QF58Hcze3p7q9YvGRyihRCJh6D/oHuvS2dlp6Oif/umfmnyzhigqFKLPzLAOyCrvwfjTaAYaZK1Wa6jF92cY+QW86uvrs0AqEolYBgua3+DgoAFQ3hEiiGfPQeVbWlpsdu3bb7+t2dlZo1Ox9jyr1zmhg4VBxkGn6YynrQGCUYeNISA7sbOzo66uLgus0um01SqzljT9wFjyndvb24rHzzvEkgXFqaUrJHOBoTx6J9tTvaDKhrVJBNPQ7j0dr6uryxrx9PX1qV6vm6NP4EzA29LSooGBAetgXalUtLCwYJk2Dxii072OQ3d6EAI70tbWZrTDs7Pz8QyDg4Pq7++3e4L9UCqV9PDhQ21tbTVkQllrf4Y4ZwBT/JxMV29vr3Wkvnr1qlpaWvTw4UMDOdljD9L4dUfO0TE4w6wzZx/ZIqsHM4W+CN7+og9hVLW3tzdQj7ERfA/3wT1xj+g0D9qgf7z8838o/Ol02uqBJycnNTIyomw2q4mJCQM8mR1NZgb9Rp0+8oDd9joUvcH4M8Djer1uHerZu66uLltj7Co6Iew54fcjBL588EtNNfIPiywej+vx48eKRCLWaJJRLL72u1qtGkWc2voQTA4zdGEA49edv7HbgDO8L5PJWAMlRpOdnJxofn6+oUkZmXj2lvVGjpFlPt9n6XkNTQ9hlaELPAuwGfDhfUIPcKF3WB8Psvnz6NcH8DrUYb6sDHnxJS8EnWHgGALA/v8+8+y/s1armW7o6uqyhkCAsZQl0bDP1057UMwDXHwHsuh7dYQgm8+Gh3LldcVlFz4qwGaz+vDPuryODmU6DKj9e/zPw/Xnd75xHbLtm+oCGDKTHjYYzRH9mCgubFb43ayfz4petnahn+Y/J7ya/QwwHR8+lK/L3h8GieHfzb47vE//u9C++niks7PT5qB3d3crEok0zJ+mKRa6D3/S+7Q+HvR7i39Wr190Epcap2T4+7sscG+2Rp8XnGl2fe7xSTgffixQ+OD8DVVneHjYFBWLyk16J9wrCl/De3h4aFRB71jRkTkajSqdTiuTyVgd4fz8vHUv6+/vV0vL+TD0p0+fqq+vT8PDw6ZQcDI8NRjqbzab1eLiopaWlvT973+/wenxCDvKF6SeP2QlcJIQUL632eHiHgh4ufb399XV1aWRkRE9fvzYqHC1Wk2dnZ3q6+vTP/tn/0zpdNoyz8xUjMVi5pwUi0UbdUBWl+wme4Ij4gPHlpYWjY+Pm0NPl1wEcXp62oCHtbU1WwPfjKa1tVW7u7s6OzvT2tqagRc+q+cDG+oMCQ6KxaJ939DQ0KcCeACYoaEhXbt2Td/61rf0wx/+UMvLyw2U1EqlYvTNSCRiXUepKfRdbU9OTrS+vm6OQjwet7msoOEjIyNKpVIm/2dn511EW1tbtbGxYQhl2IgJYAmZ8FShMBvP6z3tG6oqgAeONsopEolYhikaPR9NBHsCB5fvw6EIlWh4v3w/de7VatXGiXGfZC6p1/UoNIEXewey62vYvDLkOylF4JxxPzig3Dd12p2dncrlciZ7ZPmbOfwAAwBGPktar9cbOk0TgKDzeB7OPmwAajL5nNHRURu1sry83BCYoT8I+E9OTmx018HBgb7whS9Yw4rd3V3bb+/wF4tFq5X0exhmwD0K7AFJmrF0dHSYwUqn0w2gQyKR0M2bN3Xjxg1tbm6ak3F6emr3Wq1WlcvlVCwWrRYZdgjr7NkDyJA3hmQu9vb2lEwm1dvbq3K5rI2NDW1sbOiFF15QOp1WtVrV4uKiFhcXlc/n9ejRI9PLvrlaqE+9w+udEp9dJiCiyRmOKlkw79B6WeTiGb3sNDtX3CPNSRj5RrDiaZUeOD04OLAzQVPIUF4BUi9zCjhrvga9vb3dgBUyjtT9T09Pa2JiQkNDQwZsVyoVffTRRzo+Ptbg4KCVxeTzeZNxzgdOKg55vV7X1taW6b+dnR2NjIyop6dHAwMDWllZsUDXU+qY4EBjHM4tZ5xgCvvFWhLwEQgAwJ6dnWlgYKBhVBxBJEEm4DHnJhq9oMlCn0d22Gte42f5euC7mdPq94k97+zs1MjIiDmqXV1dxt6oVCoaGBgwJgQ1wT6g9QwTgAnkivUFCMCmAYJw4XfV63Wr1+bc+ivU3eEzSTK/A5+AgNFTlX0mNBqNmv+E883e0mOD9ZIuGhNJF6BX+Dz+fr0PGjrlnGNkFCALIHh/f1937961M+uZL54h4f9mPbz+92AW348u8+/l32Gw5z/Hf49fe1hv+NaUpIRAAN9xWSARBsPhz8P3+Xvyf/vySsB7GICAUNhnwOZSqWQ2Cx3jmUf+Cn1rv06XvSa818uepVlQ7H0Lvg8/iwa5ISDdbF+fJ4gLnyHcF/8a/+x+DTyA29XVpUwmY42DYZUwxYNSCF/X7tcnDLLD9bosYG1278/z7M8CED7v9bkywtyAd5i9wgkvFF00GjVD5NE+/zfOAJ/p64g9LQ8HG6MIYjkxMWEZv4ODA62vrzdQy6QLx4TsANkdECeCC5puINhra2s2DgTOfOhA+efge0IkKlQ2odD6ILmZEaE2sK+vTx0dHRa4S+eNscbGxnTz5k3t7+9rdnZWq6urKhaL1tiLBjLMMvRUDY9Ac38cWknWPW5gYEBdXV3mfNNQB6UFUucpSTjhGBVP6SuXyxa4eZq6zwCy3rwHQ+6VbjOHIhaLaWRkRFeuXFEsFjMlKknJZNJm3TIHkUAypLPxmZlMxv7tR+R0d3crk8mos7NTa2trls2k+RYBgke+mykMb3xDheFfz+tqtYt6WoJKfxa9DPOZBDXNLm+QQqUSrjGoPo4RZ9GfaU8blxpp7Ch8HDGAGWpYw+/28op8cE5Q5F5uQ+eMzIHPUITrHrJavAL3nwlog3HhWcJ6OQLker3eQO3yvQv4GetCAFiv160uhzrjrq4u603AZ/B6DK43sKydD4RDWUIW/O95Bs6e/yz2s6+vr+FzfZdq36CG2mYYJd7R5bP9+ody5/eE889noJMXFxe1urqqzc1NGxXm6ZZeT1/mvHnZ5ncEFZ7G6WUn/KzQKfLBNlezrAvrCqDDeDnKWLx8+PUJyxr4rNCmhO/1ep41Ry95W8t9JZNJ0/1DQ0M2oot9LxQK2tzctICaOfY0ZmQPPRXUsw88pZi+GV1dXRoeHpZ0MWvej7VC1gmQPJOGZyTw9aBXMzsHu6ivr89KYwB2YE15pxEwzu9neO78fvi9CAONy3QtuiWRSNjaj4+PK5VKqaenx+7T22gf+IZrjg7z++zPlr8nbDR+jNcvnHevF8KgrNkVBhQ+WYAOC5MqPqhgf3km7L/PsPnL28FmDrL3FZoFO6F98DaPtUMH03cFVhxg4LPWJNQdXveFQbEPVC9z9r29anbvXL5u3ncOD/3AMGj9rMDkb3PxHfjivlRKkq0xGX+YmLVarWG+d7M99L52qN+bBefN/KxwXZvtwWVrz+WTKp91Vj5rrf4u9iK8VwD1SCTS0PzN9wJoZsOf5Sfyf77P/9//vNnn/P/7+txdo70xAzn0TrDPkmSzWdXrdeVyOcso0lCHYNTTE3zzJgrgpfOsIFmH2dlZCwLJhmUyGf3Wb/2WhoeHVa1WbZZVJBKx4ePSuUKfmprS2dmZzdRNp9PmeKAMenp6bC5eqVTSwsKCDg8PDRWnFsSjqzy7zxijOH2gjFHxRssrOQxDWDdDNrxWq2lwcFBTU1MWYNXrdfX39+v111/X2NiYfvGLX+gHP/iBlpaWGmpJaT61vLys4eFhQ6i84WFPvbNRrVY1Pj6uwcFBDQ0NWedtMripVEqTk5PWZZWGWN7p9w04yFBC6aVRlp+RSuYAo4exhBoG0o5CR2bIFm5sbKizs1M3b97UnTt3NDU1pQ8++ECrq6uKxWJ67bXX9Morr2hzc1M//vGPNTc3ZzTCSCRiVBFJVit4+/ZtQ8a8IzQ+Pq7R0VG1trbqhz/8oTUXIYOwtbVlDqVvuIXDjnPinXVPN+L1KPVwb5AtHEZP9wrPJ8+GnHqlFbI6LpNp7tF3xt3a2jL6qmcXRCIXlK8Q6aTu7PT0fAi8n7HnzwTPGxon/nCWWMdkMmn6hLND52xPH2aNPMrvg3pGAhFM+rPMe/k/54Gft7W1WfMcavQLhYJyuZyNxiEzxRqQxSYjWygUbE7q48ePNT4+rmvXrqmvr0/lclkHBwc2u5LGV+wbWSsvJ+yfN6T+nPEsZCer1arVELIn0KLo0EpHWcDO/f19zc/PmzNNhsz3HfDfgYPtZd5TmmmuViwWjT0C8LS9va2lpSW98847BlDyedXqeT00mTjfMd5njUKAxAcYlUrFmi5OTEyYrfIOGvq7mUwgO8iYP4vh+azValYzPz4+bt2MfZ05n0mpjs+gsWZcnZ2dqtfrDeOScHgBOMIGQpxRX3JAc8qJiQm99tprunHjhgYGBpRMJrW2tmajaXZ3d60b/EsvvaRr164pFovpl7/8pXZ2duxsYNug4AKsYHcAfhiXdePGDdMPS0tL2t3d1e7urhYWFuzZMpmM+RN+bb3OgMLrM7L8HNZKMpnU4OCgzs7OtL6+bkAyWWJ0D6UpsVjMxqLReZqzRaDmgTUvB34tvOz7f9NQ8oUXXtDNmzd169Yty3rD5uru7jbKvCSjlRaLRbOtfL+3I8iA15/oXsardXd3W7adCx1N2U0znezPApcHFSXZFAfPwAnvC11FppCGXd7XpHGZr532oCZy789GWDfuZd8Hvf7Cl/M6C/+TNfGsCmwe1PlnAVT+PjyY45+DEhR/eSDY74Vf+zDg4DkkWYkbQLW362FgHn5muO/hWoaBe7N785/HPpENpkM5PTmQZw/8SBcNRTlb/gyFIG54hWvn9yZ8Nn+FAbX/uX8efxEI4yM1A6z95zS7r3CNmz3HZZ/pX4fu4mfoIUnmVzA5BqYedpiz5wG1EFxodl8hWNvstc0CaL8enwUE/W2C6ecOhL0zJV3MBvZZGGgNOEUrKytqa2vT1NSU1QthnMlm+kXBicN58rQ/FOXBwYG2t7d1eHho1OenT59qcXFRV69e1ZUrV/S1r31NJycnGhsb06uvvmr1w5VKRfl83rLK3d3dNg4hkUhY3QcNQFB2BJDcE0YupL5hWBGAkP/uhc8Hvd5p9VQYLhxxvmNyctJGNfisQDwe17vvvqsf/ehH+tGPfqTr16+bcgaZZz2TyaTS6bQZF54BowJ6xaHlvrq7u40meHp6qjt37qharerx48e6d++ezcrl9QSvkUjEvsM7B2F23GfVUXDeSfBr29HRYc0q+A7uXTpvfPPgwQO98cYb6u/v19OnT9Xb26u2tjbduHFDP/3pTw00odske8S+tbW1qb+/Xzdv3tSbb75p9F0oz3t7e+Y0SNKjR480Njamnp4eLS8v6/j4WO3t7Xr99df18ccfq1wuW+0yz+v31tdbeEfVd3Ll/qRG5NuzD7yy8swEAlMffPgsPfQjTwfz9OlIJGJONig42W8+H8outDfuxwfuoSMAQ4NsA4HtzZs3NT8/b6OafDd59E8sFlOhULC9gj6PLBBwe8o451SS6SFv/HxwjXz6zKT/m8zY2dmZBZBkh6F4zc3N6ec//7k1K8Pge3YLehHZ8/vPSLF6va5sNquuri4dHh5avwLqrAmOoIiy1j776hFdnouggXvnuShR8aDexx9/bHKOc+2zCjgx9Xpdu7u7ZhPI9AFQEhx5feDPXzwet9KSSCRiermnp0e12jl1a3t727pAA5p5WefzPBCEzvWBE/cBS4hMNl2gX3nlFfX19RkIFup0r89CJ7CZQ+Jfw+fQmbyzs7OhLpQZtZwZbxd9xtU7JMg0a+LBIKi82FNYGJwl7FG9Xlc6nda1a9d09epVZbNZVavnHZU3Njb0//1//58qlYp6enp0584dPXnyRO+//75efvllDQ0Nqa2tTTMzM5Zh8CADXeU5kzRmqVQqKhaL1p/iC1/4gjGgDg4O9OjRI/31X/+1Njc3dXp6avqVzw1BRa8PwwAJPfCtb31LExMTamlpMYCc7vK+ESXy4YHvcrn8KfklmPHZN3+WfF0xZx0gl89qbW218p6JiQlVq1U9ePBAuVzOxpXQNR1dPzk5qc7OTmM/NfMPPKjoy1W4Z0Bp6L2wv5ApsnY40zybDyDD4D8EQHk+QCoP/KM3/D7W63XbB0l2NkMg1+u2aPR8mgDPw+dh57i8nHgdQUmLZ6tRLgNQv7W1ZV27PcDCJAcPIHtbF96z18NcjLrs6+uzkYN+WkAIaPrPDQEIPtv7nGEZlQeuw0CV72oWbPln8P9/1n1dFrTwOdhrdBPy6H2W8LNDEMk/82X3HAacPKfXD6yhdAGUeLljzXziyrNNWD/sp2cbhs/R7O9mAMplwflnXd5/DuWS3jI0UvWzsSU1TJHwCboQCAm/K1zDz7q/Z/38WTLG7/+mwfBzB8KX3YQXzHg8bvUHnZ2dVk8TOgwe1fFOp3dOCS6SyaQ6OzsbMjOgwlDuTk5OtLCwoL29PW1vb6unp8dG8HR2dlp3aRQBNK56va79/X2b/1ar1SzLRQMonyENnUaE0itfn40L1wzDw++8UuMQNqN1+vWnPtU3v6pWq3b/S0tLNgPYGzwMAcEjFHLfdZYaF0/NZi9Zg7Ozs4ZsQzQaValU0vLyspaXl81x9Y6ZN/b+ebg3n/EI18CvVYiiIUc+y0MgRSE/9Q2s/djYmMnC+vq6Njc3GzLB3jByb8lkUv39/RodHTUZ7OrqUldXl4rFotXKIbcEttRqQkHq6+vTwcHBpzIx/gxIF5mqMPjn3nBomimOZlSg8E+4tqGjVK/XGxB/1sE7y+H3E1B748lrfSmFPxdeGbOHXulR8gD7gpp1dIl3UPg8/xnIQ/iz0GDyBz3l79XfI+vhz2O4B7w+FrsY/xCPx7WxsaG5uTmtrq427BNr498fGmKCNpza0dFRm98Xj8dNvjg7ZNk4rwTY4fr6c+VlUVIDu8Cvj28aVigUbJ9h+uAQQKPyaD3OZjMnBBn08oI+5v0wP+r1utWGwojhuQHE/P77tfVyH+qT0Pnxr+nv79fExIQikYiWlpbsnsjO8pz+M/z5Ch2nZnZgbGxMfX19n+qOf5mDcZkj7HUlwA81YNHoRTOueDxuGf39/X3TL7VazRxmso7t7e3a39+3Z2eOsdQ4wSAWi1mDuO7ubms2SZbM62nvVPoAX5JNWCCghL1EL4ze3l4DQdGblH148NmveXjOpPM56tPT05qamjLAu1KpGNjr7ZJnACBjHthj7/2+sd6+OSEgs6/rDv90dHSot7dXg4ODdj+UfBWLReuzAcjBWmcyGVUqFa2vr39Kv/nnJmBt5jgSiMTjcZs0EYKYzWQvXIfwd16GmwUOIYjk/3j6q2+01iygZL99aZx33p9Fm/bnOrxH3st3IMMAhPSNCJ+1mZ0Jg8FQH3Z1dSmdTqu/v9+AsHg83sBOCL+H93obFb6OdfaMNs6tbxzX7Ar3/bOCm/D5wj/h6/Apa7WL3iE+KAufJ/QpmsUo/j2XvSZ8Pr+Wz3pG/9nhczV7xkgkYoFwGCw3u5/Lrs+6r896L/fu/3hb7YP18FnD9Q91a7NnCH2mv4vrbxP0Nrs+V42wV1Y4KbTU5qAyexHDtb+/r4WFBTN0GCuf9YOGFo1e1C1MTU1penpaQ0ND2t7e1sbGhg4ODqyuFHouzbKq1ao+/vhjffTRR3r77betm+jNmzd1/fp1DQ8Pa2xsTLdu3dL8/Lw+/vhj6+y3vb1tnSBrtZoODg70wgsvaGRkxDLXZD2aoSHe8HmnD6cNB8930vXNIVhbFP6zLhrH8B5oJHR5ffLkiY6PjzU9PW1BfXt7uzV3gh5+7949zczMWGF8JHJOKYXSG4vFrB54YGBAkch5pm93d1cTExM2miGfzyuXy+nJkydaWVlRZ2enksmk1W7wrCC/3gh5x8cHtBhhEFiMDplKDBRNTDB2ZNfohC2dO1TLy8uG4n7lK19RW1ubFhYWLJuG882FcxGPx41WPjg4qMnJSTOumUxG4+PjKhQKWllZsZ+//PLLunfvnsrlskZHRw2s2d3d1auvvmozqdfW1lSv121+K+sCIEGHQeRMukDR6ajdzCEmkAsdwtCpDhuU4Zzw84GBAfss36DO18D6LCNAAP8OwR32ydemIf8hPdWj8i+//LL9+8GDBw0UeU9VBtVmLjLPBDADFZNsZzOjz5lF4eNwIZf+mfx5ly6YFDin7e3tymQyBrAsLy9bV0vYA56JwRUCQmR7/Jl57bXXNDc3p7W1NXPaksmk3V9LS4uuXLmiUqlkATOd871MoXt9IIxxRr/6+u56vW6gIs4yOj2dThsgxNz3o6MjZbNZW9u2tjZDlgncWXPONNks7s/bBU+1pq4cmigyAyDoA2kCGB+EhwCEdME2QNeQPU0mk7p+/boGBweVy+X0P/7H/zAQkiZJntLPevFdnrrpzyL7jcx8+ctf1tTUlGU/oRp75xtdiEyyjuEsdehsnZ2d2t3dtSkLqVTKGrbFYjEtLi5qZWVFjx8/Nvk5OTlRT0+PAaPozocPHzZ0IkYWaMAnSVNTU5qcnFRfX59qtZrS6bTW19cViURsdFfIJvNMK851rVbT/Py80um0dnZ2NDAwoNnZWe3t7amvr0/j4+M6OzuzSQMAtsws97rQ6xT2F/04MjKiq1evampqyurNDw8PbQ43+wcDiuZYYeDHH9/bBBkYHR1tALgikYgODg60uLhocuH3jrrgwcFBjY2N6Uc/+pHVv9McLZFIWH+NtrY29fT0GFgTi8WsmSb3FDIGWBMfsHsAlpnrnCefHeLZvN68zBH2YIeXYd8TxIOZ6CY+DxnhPGKnYDd40NCfK9a+p6fHfEh0nWfweX3I2lPCx56Fr8dnwUfo6OhQIpHQ6uqqAY8eKPZJlHCtfKDt7z+dTmtoaMimBfC86+vrDdlcD6DzOf7zkXcu1tpPcmDP0SvN/IrnCYJ5TbOAqdnz+n2SZCWL/N83xwvBjDDwR874mb+aAYn+/Pqfh78PGQ7+HkJAwj+7pwz77wDQ9jrDr0F4NduLy57teS7uL2RxoBP95zXLavPcl8lCM4DjWff9WcHs8wS7IejRDGz5rOtzZYRZNAwkHQtBSdfX11WpVNTb26urV6/qzp07lnE8Ojqyv3Ga6bAajZ5TUL/xjW9oaGjIml6BytXrdaNVxWIxLSwsGD0IRUDgK8m6ES8vL+uTTz4xR7Gnp0cvv/yyIayHh4fq6elRX1+f5ubmzOj19vbq8ePH+uijjyzTAQXUU584CD7AQ9hx3vzB6OjosCxRa2urNjc3rQaJIBsli+IkwOJKJpP6yle+oo8++khtbW168uSJfu3Xfs2yxLVazbp7fve731U2m9X4+Lg5L5FIRN/85jf1xS9+UdK5gEO7oTtqNBrV6OiodnZ2TPHHYud10ffv39fKyor29vaUz+e1sLAg6YIqT00saDIddnEQ2EefBeRCiXsD5zMjyEJIZeX7eS1dnpkr973vfU+x2HnjrJ///OcWgKJoK5WKrbPPLpIp7u7u1sDAgEZHR61z7aNHjyxTkkwmNTk5afVajFk5Pj7W6uqqSqWStZ7Hobhy5Yoh/m1tbTo6OtL29rbm5uYa5jZGo+e1JaOjozZOxIMzzBFGifnRKn19fUYFo1bc13ex9gTAZIXa2tp07do1bWxsaHNz00oRCNI9VS6kzfF+DAprHBpAqbFuzQemPHMikVA+n9e7776rpaWlhqDen5Nqtar+/n5zVnt6euznfsyblyfq4BhP5JUpjh+NcijD8HLrn8Nn8kFWz87OtLq6avIJoNPW1mb6gfvyGQ6+J5FIWLYTp/fp06cql8v68Y9/bODhysqKfSeBWXt7u1ZWVqxu22e3PD3c1wWytpwHz7Zgzcm4ct8edNne3tbLL7+sV155RTdv3rS6ydXVVa2vr2tvb0+np6daWFiw8Tl7e3t2zqlDBjjyupiswMzMjM2UpCaSYJnMOIE09x861d5xDC+vB3z2eWdnR//pP/0nk1MYJ36EDDRlHzx5uifyzvpzfzgltVrN6mEfP36sx48fN+wVZ4eSkFqtZuuXSCSMZt/d3a2RkRErC0C+oVlDqT88PFQ2m9XNmzc1Pj4uSSoWi0Z1HhgYMCbD3bt31dnZqfHxceum39fXp1jsfJTJxx9/rIWFBY2Ojur69evKZDIGht69e1erq6vW6ZwGSewRe4N/0NLSot7eXp2dnemP/uiP9LOf/UxnZ2fGNoJ11t3dbfsEmAjDCVvKerG+PT09am1tNcol+zs/P2/ngMkK1GtXq1UVCgUDYuhe7jOrBBMEUrFYTC+99JJu3bplnZ6j0fMO/7/4xS8sWO/r67NzSICNvGYyGRWLRf3VX/2V6SA/5aGtrU2rq6vq7+83YPfBgwfa2trS3NycHj58qEKhYLNu0ZneQUfX8Tsvq+zT9vZ2QxDvQWcPWHrnOLTTrJPPKNMfwTc/Q//yN7qlXr/oG4De87YL/QQwxDU2NqaxsTFduXJF9+/f19bWlra3t7W6umrng/ehP9ln3x2XM8b3nJ6eKplM6sqVK9rY2JAkTUxMWKnUzs6O0ce9jD8rQGRPsJ8rKyva2NiwsZue4cHZR995Gjd+EEmElpYWFQoF65ODTvFMIPSo1JjxftafMPjwetY/JzLjQWsfbHsAAF3uZcAH9yGjJ7zXcG39/fggKWSLeLnF7yiXy6avuUcvv8320gfXno3gu8hjyy4L2v0VBpgh2yAEBT7rCoP/y77HX+F3So3gBe/5rHsIf98MPGv2PM3e1+zyn/F5AmCuzz0+ySMKnrIA9Yn60JaWFo2MjJjDiVAhwJIM+cRgX7t2zYwoipa6O4LvxcVFCyrICBL8IGDUi3H5GbaDg4NmIHd3d62GDzSZESgoQKjC3lFsdvBYGz7bI6H+9wSMvb29tm7MWZY+jQCFFAqy7IVCwbKyKE3uq6urq6H9PM0cQBIPDg40MDBgHSgZjl0qlXT9+nUDMj788EOtr6+rUChYBqZQKFjwToBH0Mvlg9ZmdZasU4jYe4PI63221K+pXxNkygfNBIvMXoVex6gMUFxfF0StLBcUaAAZKPk0bkkmk9bYCHr6xMSEBaeLi4vWdY9uqDgUBESnp+djwFKplCYmJtTb22vNiDC4jOEBAArPoTeSIciAo0Jzst3d3QZwBpkguOO8SrLsdTgLMXRCvFPFXvqf+TPjz4KXCY9Qsr+1Ws3a9x8dHamnp0fSRTaimcPl34sDw8U5lmSof19fn65fv26AAdlGQLt8Pm+gF8/ng3Yvk/5v7ot/+xKAywyAJKOE+edCp6DXCCSpaQxBEfQgTdtCo+udF+7Ho70hSOWzDNwTzjOvPzo60ubmpnK5nLq7u3V0dKSDgwPl83mbaXp6emoNZmBbSDIgkTPLfZydnZnDjCNNIOr1is8oAQp5KmUoe8+6vJyzVoyy8rqYvfXlHl7+CCz4DNbRU9Y5c9z/3Nyc1STTcDAMWLzt8ewMstfUFfpnqFarVjbks80nJyfWiJIOz7FYzGqUuV8CP3QF3727u2v05VwuZ4Hu97//fUUi52NtPAjigSzAIABiMn6wELDj+XzeMuPSuf0rl8uqVqvWk4Q19k5b6JiSOYXaj4xVq1XL/lLO5bO6OPHoyGbZkGZBApnYnZ0dbW1tWSkSM66xQdyLbxjFuWJmZ2dnp/3cZ5MIJAEA6HexuLio/f19W69msv+sZ/ByGwKUvIZ78Z/lAw72wzMAWHMfXADsej+g2Zp6HcZr2F8fzGLHAeUBMVgjSQZwIJM8s+8v4GusOUP+uaDRc5ZpMBQGcuH6XLYH/J7XsC7IPXtBrxDPxkI2/B/OBiVF29vbtofYE+waetKDRv7eQ/3p9zh8jmbBjA8+WUteE56n8P+8zgfDl7EHnnWFwfllgTA+YAjY+9c2AzMue+bQL/Hr7t/zee3Us67L5K3ZfYavf1YQepk8h8/wNwlEn/fyn/88z/m86/m5u0ZzIxwenzGs1WoqlUoqFotaXl5WtVrVwMCAUQFxsv14oitXrmhwcFDDw8MNc2HJUFYqFeugWS6X9b3vfc8cJjo1gsAUCoWGhgphq3IcLuqlZmdnDcUfGhrSysqKisWiGc1kMilJZuwJ9vyBki4ONgqJ7/XOvUe9IpGI1fQMDAyY0wqd2B9Un6XkXt5991394Ac/0Pz8vCqVip4+fWpK7Nq1a7py5YquXbum8fFx7e7uKpfLGS2xVqvpww8/1Msvv6xkMqmhoaEGpPd3f/d31d/fL+m8fuoXv/iFnjx5olKppHK5bFlWn1UJszI4r1DZ0+m0GSpQ/xBpg1KE0sMZIHPAutfrF7NevbMe1kB6mYUSjqNXq9Ua6rOQjba2toZu1wcHBxoaGlJLS4uOj4/1ySefqFo976Q7Nzdno4uuXbtmNPKxsTGl02mdnJzo7bffNgfg5OREu7u7qtfP68T29vZULBa1sbGhq1ev6tVXX9XXv/51ffGLX9Tq6qrNPZ6YmNDg4KDu379vGTZkolqtmsODA0tGbHd3t4HGzTzUev286ynPCxWeJjAwJwqFgpaXl+1zoEy3tLRYMAStD/n3+4hMNXNcvGHxRtjLOgHIwsKCOcednZ0m554qB9CDHuB5KpWKOcp8pkeoqTe8ffu2NXHa2NhoCNZomISM8X3eEfGAl6dSeVAHh5e98s4m98VZoASAQJGzyXNSm9vd3W3lKcgYxpwAuV6vW00/weVlhsMHwT7g8Y6Bz050dXWZYT88PNTPfvYz/eIXv9Dg4KD29/d1dHRkTc7CizNMQM855xmQJeSCWlwCTy9T2BOCB+8Yf17HgveEgBwAKQGurx/1NEN0G2vmnykeP58lCVDBzwnQ/uf//J92ZvgezgbfF41GTdb9iKXj42NNTU1ZbwvsZ7lc1vz8vM1gJ8iMRCIaHx/X9PS0BgcHDURhzihy5WndsLnIKM3NzUk6Hyu3srKi7e1t3bt3T3/4h39oWRAPHmA/OZuUetABnAY5AFi9vb22Rn5EH5lof0ZZG+9wc67QCwCCMEfYm4cPHyqfz2tsbMxsPZnjaDSqVCqlnZ2dhhGQPgjz+k+SAQNksQHsuRKJhLq6uizDTlBfr9dtfBb2tlwuW1kT4AIgEM0+y+Wy+vv79ejRI/OZyBSyfqHz7lktXofV63XbX8AK7x+gA/lsD16HgUWtVjOGSnd3t7a2towxEYlETA5C2rXXQdyvZ674YJl19z8j0TA7O2syStlGa2urstmstre3tbOzY7PZa7WLMgMCXe9oewC+Wq1aOcDExITOzs60uLiotbU10wNhQOFlJtwL/z2RSMRKJTgr+Dr7+/vmW/nSKS7OSV9fnwYHB9Xf369KpWIAJd8DSON1rP8Tgm6X/eH+/dXs2cK18OBG6O/61/lz7EG/y74r/JkH1sOANPw+bB222fdp8EmwZpe34c2CQr/vHlz5rCD4MrCh2fc3AyAu+yy/TpcFu5etX7Pnvux3zxuIf9bPL/u9f4bnWYNnXZ8rEA6zNeEioFwJIvP5vHWARCH29PRoamrKXvPlL39Z/f39SqVS+tGPfmRobCaTsXrP+/fva3l5Wfl8vsHBg37iHWqUNs4hThaOY6lU0ujoqF5//XX19PTogw8+0JMnT4w+NjIyYvQvkPREImGdT23h4o1ddT1lCMoVCo01y2az9r5cLmcZxevXr1u9GwofuiHBIejxysqK/uN//I+6d++e6vW6jYzC+WK0xO7urq5cuWLNFQAlQP67urrU1tamBw8eWAAyMjKiYrFov7t7967eeust/epXv7L62Hg8bgGgD04YH8BaLC4umqP4O7/zOxZAPn782OaS1Wo1q23EWcE4eoqTR8ClC0PD/73CwuijMD1tHepjLBbT/v6+OXjRaNTGfySTSevOy+iaSqWizs5OFYtFLSwsaHFx0ZySSqWi1dVVfe9731MqldK//tf/Wr/85S91//597e7u2p7jSLe2tqq/v99Q5NnZWQNA6vW6fvCDH6hYLGpvb8+AklQqpSdPnti64ZR6tL5er1sjLoI8zs/h4aGmp6eNKjwzM9Mwsor30+yjVqtpeXnZzg8ZCQ+AoXg4/2HW34NEBAU4FJybUKd4UIIztLa2Zo6xR/zZOzK3sE7q9bpKpdKnAmyCFxxpnIOnT5+qXq9rampKXV1dWltbs7N4cHCgu3fvWudjSQ1GzDveHpzwTnIzpYyTwxr6bCyOEF24+Q4cTpxQdBM0Wd/pmbMZ6muPUocOGPvmzxI/47yFATtNXHgWymDm5+cbbIaXEe/gkVUCnAQEYE1bWlqs4ys/4zs8A4XgkCA/fE5e48/Kswxn+PtoNKre3l4L7lOplN27nw/OerEP7OXJyYmGh4fN0eYch0Hb7u6uBcyAHD7oBXAEfAbgIJA4Ozsf+/PkyRM7q9ggbDOjgjo6OpTP53V0dGTsFk/xZ9RUe3u7hoaGGmrzDw8PNT8/b3aXEVNenthPdKkHQmu1munAarWqfD5vgMjx8bE6OjqsUSUlGf45CZIBSw8PD+39Xla8E3t0dGQ0VmbJc44IMp4+faqrV69qeHhY2WxW/+t//S8bweUBdW9f/DkniIxEIsrn84pEIlaa4a/29nbTQXTeJ9g+OTmxEpVsNquRkRFrihfW6ycSiQb2CqUz6EieDTlB36OrwoyVPwshCB/qefRErVazbDo6GtnHv4FuShaW8w/7Jjx/fC7f67O9/N8HU/6cE1iTuACQgQHhbRhnm+SLtyt+TXhent8Hiqenpzbeh1I4Dziiu729DAMML6dc+FGArtFoVF1dXUbZpU6d9fd6S5IB/TQV5OLMMu89LIMJmXXsg5d/f4VBpr+a7U8zWWsW7IU2FL8O/zkE2kOwPfwc/u3/eNvN3sNaooQPZoC35WG22K8rNpK+B/gd9XrdPt+X7DQDHfz6XRaoetDpWcFos6vZ+jcDFUJdEK5js/dedg/Pev1nfcZlz3JZcM/rP09A/LlqhMNNCg+HP+weiSQIkNTQ4bC7u1upVErJZNLqhckmobjJLhNUe4cQyhJCiPPs7xNj6el31DvxDNQnIcQ0B/GUS78Gnl4CkhQqDl7rs8sEKfV63QyYN3A09MHJikQiZrzJcJMlI6jxgb53AEHJ/T3hiMbjcaVSKaXTaevsHY+fd6NmfMXo6Khlschi+DpDr9D5XNaM1/T09CidTmt6etqc1UQiYfR2KHfsH5/ngyUMKN/D5YMtn4WTGkel8Dmsib/8awATuLgXKOGlUskyFOVyuaGukxmle3t7+uSTT7S1taVarabe3l5JMmeUTKR0QeFElkqlkh4+fKiVlRULaqBJQg3mfnmvl/nwvmEAnJyc2Dy49vZ223PqBKnHJKjE2caQemqqN2ysH3+H6HQzY+Evr2ybKTOfgfQBWDPnBHCDwNfL5rN0FsHT0tKSdUeF0bK7u2tAFDLpsx/eePnsrv/b/7vZvYRBG5/LZ/tsWvh5HqUOnzV0DJoZW78OzRzRZk5n+MfvRXif4c9DwNDff7P79TLnHVevL0IjGwa//h69bQrl7TIj65/Js08ISrlwnn12g8wXz4qT7Kmg3pHj9QQflzlFrKPPOrH/ZN+xW9g7bGUymbRaQWQZlggOJntExuns7ExdXV1GyaQb/8HBgSRZNjp0eH3NNvdNIBLKAHre23RvAzyDgntnbavVqjF+fCADKMXaEYR5m8A+eYDx4OBAnZ2dunLlirFo6JIeBsPNzrmXx3r9Yv44tgJ9ja1kbQFp2VtACfwXdDmBVeic+7rDUDbCs+L/+J81u/xzNQOT/D7y3egPMv7RaNTGMXmAiEDN67Rmn3+ZsxvqP+nCFgGOhBRufkdg6QNcH7yGgQYXa4xuxnfkZ5znUO+EwUe4F+HvODu+7wAZS54jpAgTgIfMN+/3YCO5b/QDMtIsKAv142U6M3xNyJzwz+w/3z9/+J1e77Hm3sf2n+k/K7yXZ/0MnQsDtKury2rjpUbwh+9tJnv+wqb5pKF04ff5NfHPEq7DZddn/f6y61nn/G8SjH6e11/2/tBP/9t+79/k+lxzhL1who6Ld0JqtZq2trasaQfGm+xSd3e3enp61Nvba6gXTjjvhwK4sLBgTWK80y59eqA4wa6nZUlSd3e3qtWqZYk7Ojo0NDSkGzduaHBwUENDQ/q///f/Gt1qfHzcDFWz55XUoMyp1fEOO68h8M9kMhYIE0QTfEN9am9vN4eFz6KpF2OP6vXzhh7JZNKQZZArSdZ5k2AeqgsG9OzsTNlsVkNDQxofH7fgul4/zzpD14UyREOV2dlZQ1V9x1ec1paWFqO31Wo1ZbNZvfrqq3rhhRf0wgsvqKurS7FYTNevX9fbb7+tp0+f6unTp6YwoLn5wxg6BeyFV9woSO9MIyc+AyXJ7g2AxmdA+Bswgu+n9nFhYcFq3mu1cyo7VP+RkRHrZvvOO++oXC7bnGZmCUPrI4NBd8Te3l4bkfDuu++qUqkolUqpt7fXOvceHx+rv7/fxsWQFeKeeQ6c7VjsYqwLjeHGx8c1NDSkgYEBtbe3a2dnR4VCwTq6Qhmkdh6E3GdzpcZGCb48wu8RznQYTDQLuHyAzfvCPfIAiQ86+R0GDPTWyxEOg2+uVa/XbZ+r1aplxBjZsry8rEKhoL29PfX09Ki9vd2ySP7ycsgzhJlBLq/kPejjg0PWxD9/6KyyJi0tLWpvb9fe3l5DAOF1FpfP3lzm7Pi9CgMNDz76bAEgladZ8lkAKB4Q5f1eVkPdCoIeZvZCoAzHH5kgGEJH8sfvx2UBb7OfITPcM5RdsqI4m6lUqiHQYh98ppjsJpl+bx/QywRCdE0m0PTrhlwwfQAwLBKJ2JmORqPGpvL13HQWhtoONZl/83/WEmpgPp83212v15XP563mlwCee0eP12o1y9D6IAfgjgCCQKmrq8veyzORCcMOYjvJJHqGCp3JOResqwe/CU54Pj4XgBedubS0pPHxcb3++usaGhqyPhihvHi58c+IjAJQV6tVJZNJY97A/iG7/eKLL6per2t1dVXZbNYytuw/5+Lg4MC6sCNrrANUeOkClOF5w5m7Hlxr5niGAaA/d81+T8CLnCFT0eh5Azx6rxQKhYZAAvkB4OW8exr3szJ9nkXj193LJX6JTxbAtEulUspms8rlcrb3gJ7cQ5hIQN7QDfF43GjvnB0vC7y3mQ3098xz8O9oNGo9TMj0459xLtmXcC8pGTw9PbV6eN+LAd8aP8Kzyljvy4LhcA+anQH/8zBQ9//2gEL4mmb209OWQxDIf2ezewoDbv8e/sRiMfX19amnp0cdHR36sz/7M9VqNWvO6DuBh4G8/yz+Hdpa7sMHwvysWcD4PMFw+Oyf93WXBZ7NPi/0Ff62QfGz7tHL2mWBcbP9bgYqPe/afK7xSZIajLZHp0LnzTvlNPCgvT/GkCDO1zkxH49aTZQPBs3XChDw8uAh+iJdGEcMxObmpv7wD/9Q/+W//BdNTEyYw+aza9QGdXZ2NgTf3qHz9bz1+gVN29fgRSLnGYDu7m7dunWrofU9TUrq9brNR5VkzW5wIn0GBKXFmvhudChmMpMEX54q1dfXZyM0pPOMY29vryqVijUKk2Rdtl955RXV63V98MEH2tzcVK123pEaBBtnD4VMU6lYLKa///f/vr72ta/pypUreuedd4yKs7m5qWg0qqGhIRWLRVMMOAo4QmSjmX3pgQPvACOHvgYTeUBeeA9OqnSe5fa1fd7xgkrU0dGho6Mjlctlzc7OqlwuWyZ7aGhIqVTKqMY4u/39/ers7LTGRhhIng057ejosGfymXcM1c7OjgXyZ2dn2tzcNAPsDXVYu4jD2NXVZYFANBrV8vKySqWSNT7jMwAKotGorly5YvtKnWwzB4U15HfIKBQ4nCEvy+yZr8PyBoHP9cZYklENeV5vIFlXHH6CMy/z7DnnEmeb37W1tam/v9/kLJfLWTd8SeZA+awOZ46aM3TX2dmZUqmUzTflTIdGnXXluTyQ4Ncc3eNlyOs69BrOHg3WJNlIGWqN/V6FwJEPGljXzs7OhkwU9+idL9/wCNlgfdl3L6/ewSF4Zp98oM33EXj6TDzv8VlCH/CFcuLtgTfgHtxpdtVqNRvFxXuh7PpMAV3xa7WazayHDQOFul6va3193dbPg04etPHr6DOj6GXW2oNDUJCR11qtZuNWON+JREKHh4cql8ufArdYc94vycAVsiQ7Ozt2vgDnkANf+uN1OXbA2yzkqq2tTcViUf39/ert7bUmX9D5V1dXDXiYnp7W6empdXaGEo4zXyqV9IUvfEHd3d2KxWJ65513LNvFGrJ3Xr58wNnZ2SlJRnFlFFSYdZMuSqJ8oOhlhgZMnCn8A84yJRZQMHltR0eHjZfCl9jb22ugWLa0tDSUTLDG+Bn83Mu8l1XfKIz1YG/8+fRnGn2E3PHZ2Cb+je6ndhgb5gFZv4acUd/8zt+LP3fPcrx9MBO+3rOv2tvbbS8AomHjoaM6OzutO7fX2X5toOZ7tgrr5VkDvnQO/xJ/wIN83r6m02lLIDBikTKTSCRia8i59kE0gTHNsfCj/OX9SAJk9C7+42VBsP/b/zsESLyd8gAC7wl/5j8rDBrZy2g02tBnwX+3lyU+uxkA4WXDf68/q1evXlV/f7+SyaS+//3vG0jjexD4+/I2h3PnQShJ5sPzbJwNDyg3u69mwXH4LJ8VJDb7nX//s34fflaz2OpZ9/asM3tZ8N1Mvpq97rM+8/OACFyfKxBmQ3G+aDPf7IagnjAiJpVKWRBF+38M5unpqQ2yB/ns7Oy0rBT1UihLhMg7wF5QUc40nvBBFMEilMiuri6rB4BqzPdySHgmkHIfLONke8UGakd2KnRimx1S1tg7J/7nXkH4DI0fA+Gz0ThZOB7JZNKUMGuPAeWzqL/D+bxx44aOj4+VSqXU19eneDyubDarra0tlctl7ezs2KGPRCJWW5xMJpXJZLSzs6P79+/r/fffN4e6VCopnU5bNoXMKMBHvX7e3Gd9fd06YpJtwMng4rsxwsww7uvrM8rZ8fGxzTQFJGBvvEPp1zZUtGdnZ9rZ2dHGxkZDlp2uq319fYbMT0xM6IMPPmhoNMO9egOLjIJaI0ueju9lis/hvT4I8IExhrVarVrdlqSG7AGjwFDSfBYgDWeOPeOzffCAHKMsOzs71dvb29DIjHo3ZIyMmVdmnDEfNPrfh8bRr0EIxHkF6I0o58V3Zu3t7bXO6plMRplMxgwVcsj3c5/+SiQSSqVSGhgYMMeqWCzae/1+hZ/j6wa9kZRkhjgejxtDhfX3Z58giOfhD2uP7IWUv3B9wv1E1/A6dAv7gzMWBpLeeQp1mA8a+L3PZEgX2YPwfHv9Hjq84Wf4e2n2b14byom/vDxyT4lEQteuXZN0MeuSzyYgAFQZGhqy/gYEvFCWuWf+NHOAcGq9nvBryDOzjn7deG24zn4/AFX8mfZZQu6BHgp0jaZkye+D/3xPrfRZSa/nPIg9OjpqbAtYYowW7OzsVDweVyaTUTabtRm6ALz4HD64GxkZ0eTkpE1Q2NnZabg/H8iF683a0iCsXC4rl8tZfbKXnVCOmsmeZ+sQBPk9Qsdwxv29hY28PGgWBgEeYArvpdk58TrIy4v3HfyahQG3b3TlAxpeA8jigWo/OtGfMS7/nT6w97Lv/+/1S3g+uNCJ3jf1zAD+D6jg9zMEW/33hsGhPwvPcr79WfXAF/oaXd3Z2WlACfXTMPv8fYV6g+fxWWifOAovQGg+MzzH4b0/z/WsZ7/snDR7HVdonzyA28xvky7Ol99Tfh/qXp6X9xwfH1vn+8HBQWUyGWPGhTqgme0Mn8XHIv73Xrc/z/pd9rvnDfaeFRh/1hWu4+d5T7Offx5ZavY5YYD9Wa/7PNfnapblszTQNMh8+ZvC+KXTaY2OjlrmlUCMJgq+IcbW1pbVH9EQAMMNHUi6qJuUZPQi7wBwtba2KpVKaXJyUjMzM1YrMTAwYAg5WUqUR2dnp1paWrS6umoUNW8AmM3KGhAogF7T2TKZTJpBxSnie1gjX0PmFSRKPAyGvAHwGRBQa9+Ah/eyxgRbZOrI2rW2tqpUKpnzTrdhqIDd3d164403NDIyonr9vEnP6Oio1tbWVCgUtLq6anU/0WhUExMTNjcxFovpww8/1OLiou7evWtrWalUND4+bgwBsobd3d02JxIqGTMdw3EWrMnx8bFaWlrU09Oj0dFR3b59W7du3dJXv/pVRSLndLKPPvpIc3Nz5hwtLCxYYOyDwGaOPE4dzu/jx49t9uzS0pKGh4c1Njamr3/961aT9PLLL+vP/uzPtLu7axlQqDUwHySZfLW1tam7u9uQTS/XPutFowUCX6igHvgg40MWsK+vzwwetMlKpaL+/n6rgcUYS+dzRLnfgYEBbW5uWo2ul9PQ4W5tbVVvb69GR0etqVy9Xrea23w+byiop72yn75RRahH/JkOgyxvbPx7fXDo75czEI/HNT09rbGxMWUymYZxUV6X4dRh2LxTS/b/9u3b2t/f1+LiojVc88AF9wG4RtNA9thnzCRpenpamUxGkUhEc3Nz1riL13N/BNToDe7Z13TjsJItCo2nd4gx0NwH60vGERmkdhyn19Ou+TcsBp/18VfoQCBbXia8A+Qd69BRv+wzJTXsV7OrmWHmPCQS56P9enp6lMlk9Bu/8Rva3t5WoVDQ+++/b/d0dnZmZ5FRXGQYOWPMVQ+fLwRIeV4PGvBsodPKawE7oCKz3t5Z81lBzroHtdlLD6ixz+Pj4zZf2AfMfv0Bm7zO9M8AcA5ToF6v60tf+pKNtEGndnR0KJlM6urVqxoZGdFXvvIVHR4e6sGDB5qZmZGkhgZl9LVYW1vTnTt39Oqrr2plZUWSjGUVgiIhjV9qBNZhIy0tLVmPgFDGwv+HYAOfSeYQAFA6zxCRcU8mk1pZWTGfiH3B0fcywZnzwJk/f81k2QeYPvvqM9WhnyFdgF9e57OWnFPPUkDn8of7bGtr097eXgM1F7kJAQJ/pv3Z5zmaBaKhI+zlvK2tzRgd4Sze1tZWA2kpM2CffEM2viMEpbzceH3JvTYLUsN19DYG5g4A6dramnK5nMrlsjV45SzxPh/EebkO9Z3XI37dyBj7IBGfuxnI2Uy2wnX3digEL/zahD971uc3kwfPuvBr4O/b+9Ae8PO+Ns8Nu/K9997T2NiYbt26pfHxcW1ublpDQS+j/h6bBe/+Cm1uM6Dlsme/bF2agQbPczW792b3GwIIz7rC1zzr9eF3NgMQ/jZXKHuf5zOfOxD2xqO/v9+cd19DgsKNx8/nmF27dk0jIyMaGBiwLDDBGf/GCAwNDWlxcdEO/d7ennW3nJycVDabNYUaOnP+Z74RwM7OjnK5nKSLwIMRNt5g8TcIIbQhX3tZr5/X5tJkanJy0mrFGJUQjUYtA51MJm1W8O7urh48eKDR0VELRjc2Nuy7yQBFIpEGown6zcFHUZJB5CDjdFYqFd24cUOTk5OamJjQxx9/bB02h4eHDWEulUoqlUp2/8vLy9rf3zdwIxqN6v79+7px44bW1taUSCSUzWa1u7urDz74QF//+td1584dAwegPs3Pz6tarWpnZ0dPnjyxcRiMLmhpaVE6ndba2pq2t7f1rW99S5OTk7a+q6urOjs7n6/47W9/2wzW06dP7Z6hOTF+YnR0VGNjYzYOKplMKpVKqVgsKp/P68aNG3rzzTcVi8VULpetUVehUNDMzIzVyUqyLtg4aIAcGM0bN25Yh87W1lY9ePBA7733npaXl/Xmm2/qq1/9qr7yla/oS1/6kmZmZrS7u2sznbu6ulQqlcyBGRsbs7NAt+14PG4UOihOZEL29/eNsk1mPRKJaGhoSL/5m7+p8fFxnZ2d6e2339bS0pJWVlaMNt3d3W3BeLFYNHkbHR21+a4ErR0dHeru7tbk5KQFkj6rCnBBwDM+Pq7e3l5lMhnLxPf09Kher1sN9ccff6yVlRUtLCyYc4K+ABH3mWevU3wAgZH24AB0PBwdn+Gp1+u6evWqUqmUMpmM3njjDY2Ojqqvr0+5XM72e3R0VEtLS3r48KEF1dVq1erycUR9Z2OCX0n68pe/rL6+Pk1OTurP//zPGzrfc//Hx8emg6B5ofugS9Hdm1p0RjfFYjENDAyY87i1tWV/+vr6TFdBN02n0zZahaABHelZND7zzdqzJ37cCFRvxm6hezy1rtmcUOSFbF9ra2sDNXR7e9uyHf79nq4fAhuh/kcO2HcPDITsAx+wE6TgMAEMch5gMSSTSQ0MDNiorWw2q48++qghG3d8fKzW1laNjo5qY2NDR0dH1i+B9enu7rbGTM3saq1WM/omz+gDHpxUghHWkNf6fhX0mOjs7FRPT49ld7G7PmPE+gCgwHTAVhDIeAeONevu7m4AJAERYrHYp0YhIYMwZejyzqzuQqHQUCPd2dmpu3fv6t69e8rlclpaWjIQmufnfguFgh49eqTu7m7l8/+/9s6lt63k2ONFPSyJph6ULEuWbNnC2GNPZuyZBAgQBAGyyC6bZJlFFvlQ+QwBgskqqwABAuQxQZAEGY/tQLZsxbL1IKkHKVHvB8m7MH6lP2sOJc29uIt73QUIkshz+vTprq6uf7161dM6eN/u7m4rlUoub9nn+SGyCRmMEXVzc9Mjv9RTCUWFi/1YjZNEDqGMs/Yxsik4QMYhC9WQEec/giqNQInggf7oCQHkhKvM1XeKhmAtNMaaU1BK1EOtVmtbd2oMUbkeDTrMJ2lbema1/vBMBTP6LDXI6HioMYHxQ/agZ2kKRgSeUU7qOojP03a4Dh4iehC9TUPd19bWLJfLtRVrxcDO2kJmqxFf5/rk5KQtXUL7Bg8p4GeO0c90Xrj+PIqgTHmSdRr7EIE5v7NAVATMvDcyTwGv8pcaMrRPavBVAxbPKpVKrqche9HZ47vGvzvtUXyG8YG21ICVBQ7PA3Hx+qyxOw+UxrbV8BCvy1qD0ejRqb2L+nGZ/maB//h9NL5802d/4xxhs7ONSIULSjPWuOnpafvggw9samrKQTOFMWBUvGF4z8zMva6ciWpmntOoSpgOAIobggClnuqWWdYntWCS16aKEe+CNY/35cgQwrfNzENJeQe1QKG88/60Pzk56Yo8IXRsgggkPKS0aXaWJ6a5JZpHhxf8008/ddAB2NI8Fjb2Uqlk9XrdTk5O2iqFHh0dWaVSsbW1Nc+dIo/4xYsX/m43btzwMSyXy77Qt7e3HcTr8TsqmFDcUd5v375t+/v7Vq1WXTD39fXZjRs3bHh42K5fv27NZtPDn2/fvu3g9/T01BXPfD7v5zITXoTVdXh42JVCxmt4eNjD7K5evWqVSsWq1apVKpU2LwmbUHf3u3yikZER6+3tte3tbXv+/Ln19/fbz372M1cSAbUsTiIiuJf/zb4e2nRycuLgnxAdNi2APkeCALI4ouX169f2xRdf2PPnz+3o6MjGxsbagCKbJXnKhOExRmZn3lEtNsa7cBb49evXbWJiwo+cwnDFudEo5n19fW68QHjGkE6AB2PFRgWPswaYD3iJe9SyzxiPjo7a9773PSsWizY8PGwffvihDQ8Pe457vV633d1dGx4e9o1TUwXUQ6qbJ+CQgn7b29tWLBbtzp07duvWLdva2rKjoyNfc3o/mx9GQM1DvXLliueu4U3h7FaKn1DkpVQq+QZ7dHRkBwcHnp+KLEE2azGbqCgqf7Za76I+ZmZmnA+wlgMOdX7ivqCbEXOBIa5QKNjVq1dtbGzMurrehUwODg56X83MUyFIY9HNT5+jyk+kuCnG/2OUAAoM32tYL0bDnp4eK5VKNjQ05GMAD2voKlEfRH7gWTY7UxCp/k9f1Cumsj6+A3sfa4C5yXpv1gDGZwwN+XzewR6gTMNvAawaiqmev+hZHh4eNjNzsMjzAMBajFCNE11dXfbq1StfdzF9ZGdnxyMhKM6lsolnsFdhvHz16pVVKhX/jFSnrq6utsgPgDL3a8SDpozEkObIh8pDnXiR++Ncwg9q6GPcIxiJ/KBAKku3if1U/jhP0Y6KZPQaR1nItXoN8i1LedZ3imtYDYPoe7GPel98H+0DBj8FJrpvxHmN0RexbVXEIwDQcdDvIqmnX730kfd0T2OcFcTFuYz8GA3BMRRX9wFdz508npch5e1OPHMecMsyNijp91nvrP1Qw0QWyNbrlY/NzuQxVcV1vcW+sy47jQPzq/1RIKz9z1qHWTLlPGAc771o7iLfntdWp7Yjr1wEhi96z07vrW3F9Xfe83i/y9B/yyPcarXnxWpoFYeZ379/3x48eGCTk5Nuoerr67OhoaG2oiGjo6N2eHjoRxQA2gASHPGCcqFKtFI+n28LuyTk9+DgoM1LoMIYRUxzdszOCmypEoMgrdfr3nahULD+/n4bHR3196Kir25g/f39ViwW3dPcarW8aBX95kgbFOiuri7b3d21Fy9euMVa50JD2szMj54aGhqymZkZ+/TTTz20GGUai+Lx8bGHVS4vL7eFs6myvLGxYeVy2Wq1mi0tLbkC/9e//tXq9brt7OzYgwcPPBd1fX3d50erow4ODrYBLkDL4uKi3b171wqFgt27d8/DgP71r3/5sRUYSwCrAMFr167Z9PS07e3t2e7uri0sLLj1Dg/qzs6OW/WxUpO3PTIyYtPT0zYyMuI8isdqcXHRXr16ZeVyua0YDJWi8WhwhnK1WrXHjx/b6uqq/fznP/czgKkoqcVC8NDiqdDvUE4pXlapVHycUTKp/koKwebmpi0vL9vNmzft0aNH9vDhQ6tUKnZ4eGhPnz71ojFYvFF0zczz8Vh7HPFlZra6utpmZFHDy8TEhE1MTNjDhw+dj8g77uvrc48wR0+haHNmNBZoxgbPU/RqqNEnguNcLueAgiiBfD7vyv6tW7fsW9/6lv34xz/2UHsqqLLRra2t2c7Ojn37299uOxYG/tdia3h3ABhsmkQeDA4O2vT0tH300Ue2srJiGxsbfn4nRg4tllYoFCyXy9nLly/dW3r16lVbWlqyWq1m+Xzec9L5vru7265du+ZHuzQaDdve3nZDyfj4uI+5jiVAGG+AKglRrhcKBXvw4IEfr4VBC+CvobWqrEYlhnnK5/NuiBgcHLRbt255ZA6pEfAKlnhCR2k7S/HLUoT5Hd9J+4N80sJ7uVx7BXlk4fHxsUevYKzj+ewH6lWlqJQCT/qAJ7RQKLjBk9oYyusouzq2uumzFjHI6Z5AO7lczvNtibZBfsB7FALkbGT1iB4cHPicEhLNMzFuUNhHTz4g7H95edkNaxgc1WBlZvbll1/aw4cPbXp62seE98Ogyd5PvjDz0tPT42C21Xp3BvjR0ZG9fPnSqtWqy0jmvKuryyNwiJxgD8fziFzibHhkMvIPj6gadCP4irzJ3xgjmAfVMxQIa+6zGtB0jtXIrtWz+S4qflmgkTWg/8dIBDXkx+dHPUrfWdeZjgP8pW3xrkTBsXdsb2+3GaCjsaoTEFe9CyMu763GFgWAAOMsI6u+XxxTHbcIkLLAOWsALzzvo3MGeEK3NrM2b6QWUozPimPBj+ZFa78UkJG2wDtkgZ1OfMVcqpzl7wg0zxvT+HkWKII30edjm+cB4WhM1/6pTGf8o/zV94JPYpSE8j9yQ72/amDUvkVA/U0pgtnLGjC4No5hVrvnAUrlmcuA8HjveQA26/lRvsb2LupvFn2jHGEEY7PZbPO0AsgQ7IVCwe7cuWNTU1MezrS8vOwgjwT009NTu3HjhuVy76o5P3v2zCu2UvIexRUGZ+M1az/cXD20h4eHHnIyOjrqGz33MFAoMYCTw8NDD3MjHKjVajnQQ9mnHUJMqtWqV1lVL3lfX5998MEHrsianRVteP36tYNT+oBXb3Jy0vr7+61ardrf/va3NkugeoPVo4yg39vbs5WVFZubm7Oenh4rFotWLBZtcHCwLRSXfOif/OQn9uzZM1tdXbWlpSW7c+eOK84LCwu2trZmg4ODNjk56fNGdeGdnR1bXl5uC9cBcGkxFA2T6+5+V1G10WjYf/7zH/vlL3/p/FUoFOz69es2MzPjShS5YwiYoaEhV97MzJ+5v79v169fdy/D0NCQ9fb2WqVS8XBoqiZT6IvQIRZPf3+/h2ZplIIvlp6zsypVcQQQN5tN+/Wvf23//ve/rVaruWGHkGQ2+GKxaLOzs24gohKrAjs9m9Ps7GggQByK+fb2tn3++ef2+eef22effWY/+MEP7N69e/bTn/7U/vGPf9jCwoItLCx4Iavu7m4/J7dcLtvs7KzNzMy4N359fd2VScCXemvhqVu3btmPfvQjW1lZsa6uLpudnfWCZFtbW24I+Oqrr6xcLrshC6+PFqJRzxObnCqDzBGeFFVgGo2GV5rn2cfHx1Yul+3Vq1f2xz/+0YHrF198YaOjo3bjxg17+PChzc/P2+rqqv32t7+1tbU1q9VqX4sgiQoiSjHrnWcdHx/7EShEqCwuLrpXkHw0QnABqz/84Q9tdXXVDR6ffPKJjY+Pu7KPsntycmLLy8u2uLhojx8/9hSNw8NDGxgYcMMehewGBgbaNnXkjkafsG6jYra/v2+lUsmPUtOK6FrJPm6UjBWKNEfHra6u2ps3b6y3t9e+/PJLD31ljFn76kml7fPCx1ThQrHHE5S1GWtNCdaeeniRpcwZY0jKAnvb3bt3rVwu+75Eesjy8rLLbjyRkK6f+/fvu5FucXHR9zROKaDiNsYi9VzCfzo+GBwgjDX1et3evHlj4+PjbnBDrvLDmtJ7kc+a1272ztg6NTVlU1NT9ujRI3v9+rXt7u7ayMiINZtNB9tmZ7nWkd+2tras2WzatWvXrFgseiQVa69Wq7msMjPfZ8rlskfzdHV12eTkpEd1lEolm5mZsTt37lg+n/cq3rw3P0RhELWm42f2zpChwA/QEovz6L4Q+UwjXdAPVHHnu66uLtcn0Bko2qnVh9VrSF9Yg0TB6TW0z/qIhhRdSxplhrFOZa5GKSm4UAMSclhDx/lfI/iigq050cjTQqFgm5ub/hyt0aD9R25FuRCBkOZaaz8U1ACS8MhmPa8TOIyARnkiAlSeDU+wnzH+sS19Lm2qPFRDQ5xfjQiKPAD/w/fUA2GPxmCrvKzvcN67op+T6qUnecR1ch5QydpbaIO9LIun9Lq4D8Q2GXfthwJX1gTONa7TUyzYKzRah+fjfGCu4XM+06gEndcI4nnX88Y9a1zPM+Bc5nt9xmVAdqc1ctlnd2r3IgCua1pJo0EuQ5cGwtohFkqrdXZkCswAyNLjNwh5Y3MEJA4NDdnS0pJ99dVX9s9//tPm5ub8cPtYiEVDlXlRBB0bARuEApxWq+X5SDC0WuRhXtolR0otOfRZN0A2N7X+8DnALZfLtR3FoGGXKBm8E2NC6CP5iXiDI4DXBQsQJmSsVCrZwsKCF0s6ODhwZY0NlD4UCgWbmZlxMEfBFfILKYaFIqM5zIwXAgUFjflG2cWyj5JL9c+Yf4LxghxWwgjJZ0Jx4GxcBfe9vb3uoT85ObFSqWTlctmePHlia2trVq/XbW9vz0Pu8FjDF1qVvNU6Ox5Lw4YQdvAZ9w4MDPgcrq2t+b0adkg4IW3U63XfCLlOlZpOi1jvZb0NDAzYwcGBzc3NWV9fnx0cHNidO3fcALO7u+uKOcXtEMyvX7/23MeZmRkPc2adq6JIyPH169dtaGjIarVaW4h2vV637e1tq1QqVigUbHd31/O+lWd1w1EBmPWZKl1cQ1+YG9rkepRxzkpGRh0cHFi9Xve5LJVKtrm56ekUKK2dhK/2gQ0KkEv+J4X4UHjoP8qH8k6z2fSq2ngBRkZGbGpqyiYmJhwYkIPJWl5fX/fzTzc2NnyOCPseGxuz0dFRP3d6Z2fHFS8UPtaiRrCcnJx4zqZW+4Z0c9HIkThvupFTQExBnHrFmDs1TAJS1XuDrIuKnSoPzJH2kzWqCgzGK+ZDlWGNtuG5yCIidmLBNxRzPK48Vw09ZuaREMgL8irh5fHxcQeKKJEqf6CskGjtB0ogckXPruVzxkyNqMh15CnvhBGXsTw5ObHNzU2XoYBqFMNCoeByEVkC/zAXN2/etGvXrtmVK1fs7du3rhwSQp/P521wcNDu3r1r+/v7HrnE3q159mbmpz9MT097Ognvj2xm/InEoU+Abl3jkX/QHaKi2gn0ZBmGFMwoX+jfKv9V0dPnqocw9jvKrixwpYqj/t3JgKT36zvH33qtjlEcJ8aUta57IkVTaZc1r5407Ut8Lv/TZhxDlSNm7ce4nTd2cW7jNVnjdd6cRMASxz7rGRGcx2dF+RvBopm5gU8jONFvNQKsExi/6N0xQMR37TRfsc2sMcvivTieem/8W3WIy8yzAlNklvJfJxB+EWjTPUXXfhafnddWVtvn/X8eXfScb9LWefdkgeqLro99vOx7/nf6fGkgrBOnuQ66gJrNphcGArSgiFGFstV6F0ZVKBRsfHzcfv/739tf/vIX+9Of/uQ5jWZnCgBtU7wl5gmbnYFPQKVukI1Gw72fgGvCnlQh53iIK1euOBBWxb3VarVtvgpsenp6PMQKLwhtUoiK0OesSYvMODQ05ACP4mEazqH5TWrdxguwurpqPT099v3vf99OT0+tXq97WCqLGOW0u7vbi+uUSiUzOyvdj+eV3DeMB2bmij7hn2bmRgzyvOADPkep3NzcdE/X2NiYtVotq1arzluEFBNSdnR0ZIODg66oEbqGwnX16lUHfa3Wu3OA5+bmbG5uzp4+ferFr9j04GWUYxRvALBa7qLCAIjQgmWcjZvL5dw7Sb81DJn56+rqcsOQKiZsQvQxCl94kfHp7e21iYkJm52dtVqtZqVSyZ48eWKnp6f2ySefmJk5T+hRUigCp6entrKyYjdv3rRCoWCPHj1yQ8zu7q6DGNYOa2NyctLy+by9evXKVlZW/MzDjY0Nq9Vqtrq66gB9cHDQc1fpv+a2Mba53JmHVy2wCoKjIooypV7rXC7nx62R206OcrFYtK6uLq9ECx/t7Oy4YqChaJ08PwAoFGtC8be3t7+Wrw74I2IGCzG8Wi6XPRwbJWV8fNw+/vhjW11dtd7eXqtWq9bbe3aOcblcdlC/tbXl535jSCMVY3193TY3N9s8vwB98sRrtVobON/b27PFxUX35KmnJyq3qpgqONNQcMJskeUKKJAjGMlYD6xLzRXW+c9SoBQIq4GEdUXqA956NfaYnYFRjK70jTZ6e3s9ioR3ImqJOR0aGmpTwAE1jUaj7cxnXfMYSQYGBmxqasqBKicnKBDjHh1DHRs9nxJegd91rDQHEb5j79PaDMgPZBd8s7W1ZfPz8x7+THVt5o2j65rNZtuRauz7w8PD9p3vfMdl/c7OjvM4cn5gYMBu3Lhh9+7d8wiq9fV1l0ubm5s+bt3d3X4fIdTNZtOPykNWan601srgM8aecWPM1ICpxnMoAiwFY7Spsp45yjICahh59KaqYQjDlRpKImCLCqEab+ElBQdZ90RQG2VxlsKq/N8JOCnQ7erq8jD9SqXSxq/0SY2psX9ZoLCnp6fNyBWNZtpXnQtdl5HOAw2dxkKBvBpC2H8VCMf5jvN53nOU9FnKk61WyyMPtbgdhKzUqEudg8ivWbore6PKmAjsLhrH+LeO32VArPY5jkdWG1n3x3xurlEnheoIWcYxfQ5GB+Y/GrX+NykLGMZnRoNHvKaTweJ/0g/l5cs8/7zfet3/KhBW5lZreK1Wc+WaQjlm5p5HPC8UzGITWllZ8dDYly9fek7i4OBgW04vAxIFBj+AQt1gAKkIm+Xl5bbwLAgAo56K7u5uW1hYcKHARovQoH/cQ5gnXuju7m7Pq9vf37eDgwMXzBSNiiEZTGCr1XIAuru760o5HlqKfwB8URYpqoUCx1jt7OxYvV63er3uCk13d7fNz8/b8+fPbXFx0ebn561YLHpOmXo1aEuFJ8c/aLizCpiBgQEHlqVSyZaWlqzRaHi7KysrDt5Rts3MisWig3x4YWBgwI6Pj91z0Gq1PPT18PDQ5ufnPXcRkHF0dOThoYSN8t6qJGsIEOGI8Db8l8vl3MtHSDleM4roaH5sT0+PjY2N+TFaKysr7o0lPJexRTlCQUYoYlBRcAHYVg8ToTvlctnzYxG0pVLJfve737lhaWhoyObm5nx9MT+NRsOGh4etWq3an//8Z3v69KlNT0/bzZs37bPPPnOlD88zQP/t27e2vLxsf//739vAJV42wt7gZ451AoioYsL8Mf4ondyvG3kMiVMFttlseo43RdsajYbnBdbrdcvn875uyeHVY8WyjBDwi1Y1ViDP3KgnsV6vt1WFR+6QB8caxzCEh6vZbNpvfvMb+8Mf/mCzs7O2vLzsRc7Ua2V2ZjDCS7m7u2vPnj2zhw8f2ne/+137xS9+YZubm7axsWFv3rzx9UH4O1ES+t75fN6Oj4+tUqm0pYOcnJz4/RSAi+tEjZN43Bg/FAmMX/SXEDo808htlLNGo2EDAwNtihzzEmUU8wSQVQMWvMY4Y9jTNog0YV2yvtg3nj175rIOoEZeN30jkgnlhrXN9azT+fl53zcPDw+tWCza+Pi4ffTRR14/gKKMXV3txdnw4OuYRPDFeKgcYXzYj7iG8WYNE1ECX9RqtbYx1n1Uq92PjIz4el5aWvKxVP5HtmWBDPZI1gNjzZ4cjd9qJG82m/b27VvL5/NeQJEUCNrmmEZSIDBkRWWWaA4zc3kVjc/0TfuUpfCrzoI8i3oNugT9oa+q1+j1Gq7K99FQwm/lf+5XuaXKuEa46TrTPaiTx1gVf+RgBGCRP1+8eGHHx8e2sbHhaxJnBNdrZWPNYY3GtAjAeTftq0avRA+wjlEWyIzzqKTvpddGZZ7nqQ4Qn63Xx75FMBqvU0OGtqd1KXK5nBtQib7b2dmxtbU1jwBU3qHd6InXZ8Z+qtxkffGTBXDi/8pLncYyvnf8PI5nnAPWe3zP+CzdO3g3NUaZfd04rM/UXG+z9oramuqi0U9qCIvjnfWMi0CuXtfp+ghys3CXXpclT7Kuz6LLXnPR9/A0/cp6n05jl0WXBsIKIAlBhdEpHDQ5OWn379+36elpm52d9SIYKDMADsJUt7a2rFQq2e7urjONLjwlTTRXb7Feq5sVbenmFdtUhlaLL0LTzDwX1ewdI2tFQ65TgK0WJYwHLABdiJ2EggJlNsru7neFhsbGxjyXRp/HNYwJAu7Jkycu4NbW1vx5Ozs7ngu6v7/vmzA5UShImsPC341Gw4+nAvyrVTdrg8rlzvKsst5X50MFUKPx7jxjvAtYKgG8hKTiuWVs6Qegi7ZjbokKOC1Uwjtx/MjVq1dtcHDQisWiP1tzC3m/gYEBe/DggW1tbdna2potLCy0PQt+4TMFcvq9WlOV/xGSXKMhrYAl+Hh9fd3zcHXjx6MF6Xhsb287UNHcFAWnbJyEcGphCXhSN0CMFCq4VMEDnGQpU2rQYiyUvxg/nh8Loqgw1NQJQiUVuEV+VaVP54T5o39cS185mzwe9aZKWFwjWIvhq62tLVtaWrJqteogNR6pwr0a6dBqtWxtbc2ePHliv/rVr3wNMcakLGxublq1Wm0LH2X+eE9kps6f9lkV5LhZxs91LjTKQtuNXiSMD4wx36lnSJUS/TuGJENqyAUI00dkuOaDZilxXLu3t+fFHJUn2B+Q2Twvjj8VwflsY2PD5ufn3XAJr5pZm7FPjTBZygtjH4GZRj9xjUYsYOhV8MFaYW40j5L9jfHe2tryvzc3N9tkncreaIRU4jodd03PoO/8r7ITuYYcHhgY8Igh1hepG3oud7VabdvPieTQNaDeWh1XlUmMqc6D8rR+r/qKtqe8p/ecp9gpWGUs9H4l3WP4X2VpVHCz1rmu6SzKAnX8rf0qlUoeOaZrT4F+LOyX9f5Z4xF1qyh3s7zKqp9l9V3fId6bpdd0ovOu69T+RW1d5hrWe6lUskaj4bU9zM6Mu+g2GkGkvBD5vdMY8bzIs/p+nfrcaS1BnUBuvC+uNd5T24ntx3uzMIPuR/H9dG2wzjAKb29v29OnT61SqXgtAr2f50W5fRFfxWdfRJdtT/+P8qTTvqP/x/2o03O+6frp1J5+By5Uh+1F9I1zhMkBwnLd399vExMTdu3aNbt79659+OGHNj4+7pVjDw4OrFAoeOGXWq1mb9++9bNcOadQmQEBrFYm9Uqokswg8h1hrVmbZi6Xc29DFPIISq0EB6jSzUMV3ywLHX9HBSoyVMw5UksuQCKXy7nHamhoyG7fvu3hZWpd59pcLudh3/V63RYWFnyzbTQaHqqrx6BcuXLFDg4O3BoIcMrn8z5WKOHcowtWQQN9MjvbWLHYqyVdx4hxVJCnxQcODg5ccaU6LhsnR/y0Wq22I5Kw5LdaZx5FFL64sTLemm/ebDbduFMsFm1kZMRGR0etWCz6hs2G0WichdoXCgX7+OOPrVwuO3+qkqVKrC5QBYjKt9pXimfp+mD8+Q7gYGZWrVbbAC2eDRRGFF8FdOSfEmateXW8K9Z7xijOM7xNX3Xeo/Kby+XaQkvhCeYED2qWd4Tn0HflGX4rv7Za77xVtKHFjFQh0LWk61fHm7GLQBhvMAYINdQx/yqD6B/jDB/iKSGVgrDqqIzqekXZ53zhxcVFGx8fb+Nb8pi3t7f9txoPlXS9RD6l31F+6rzGcDLaIb1E0xF03NV7Sag6/6uMxwjH83R+NFeWPuhv5aMI3KOypHOrfKLF37gWryN8qxXn2b8ODw+tWq22AVtqJjx9+tTMzrzTCrxUOc0yPMS/1XCjFNeQGq8wqGhxSsaWNaZ7FnOxt7fnESOtVsv5VgtxEV2l6xSZHw0tvAfrTPd49aJoH9njq9Wqyyxy5vnBAI/sNrO24kw6zsxz5IesMYU6KYW0xTvxXTTM0qe4zqKxOO6h0Uincxz3O8L9o6IaZVIWeIm8F9uISmcn5fn09NSPuWIv0vGHdD9UQ4jKHW27E3iAX7P6Eq+LFNdL1jXx+6w12um+TsA6q93IF52Au5J6yVutlq2urlqtVrNKpfI1mQ1/xPGl/fPGTcGjtncR4Ik81Imy7u/UVhwXvU4N/HEv1WdkySLFEVnjoriBsSWKplar2ePHjz0VStdLXM+dxibyS/w+XpPFX53GVtuJn3Wa98vSRf24qP2se6MuxGcacRGjL859Rut/+paJEiVKlChRokSJEiVKlCjR/yG6fH3pRIkSJUqUKFGiRIkSJUqU6P8BJSCcKFGiRIkSJUqUKFGiRIneK0pAOFGiRIkSJUqUKFGiRIkSvVeUgHCiRIkSJUqUKFGiRIkSJXqvKAHhRIkSJUqUKFGiRIkSJUr0XlECwokSJUqUKFGiRIkSJUqU6L2iBIQTJUqUKFGiRIkSJUqUKNF7RQkIJ0qUKFGiRIkSJUqUKFGi94oSEE6UKFGiRIkSJUqUKFGiRO8V/ReNdq3kZ5twdwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Decode latent representation of the intermediary images\n",
    "decoded_images = []\n",
    "for image in intermediates:\n",
    "    with torch.no_grad():\n",
    "        decoded_images.append(image)\n",
    "plt.figure(figsize=(10, 12))\n",
    "chain = torch.cat(decoded_images, dim=-1)\n",
    "plt.style.use(\"default\")\n",
    "plt.imshow(chain[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
    "plt.tight_layout()\n",
    "plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e5e2262",
   "metadata": {},
   "source": [
    "## Cleanup data directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad55465b",
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "formats": "ipynb,py"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
